BaslineApproaches.ipynb

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            "Requirement already satisfied, skipping upgrade: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy) (2.0.5)\n",
            "Requirement already satisfied, skipping upgrade: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (2.23.0)\n",
            "Requirement already satisfied, skipping upgrade: wasabi<1.1.0,>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from spacy) (0.8.2)\n",
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            "  Downloading https://files.pythonhosted.org/packages/a2/53/97dc0197cca9357369b3b71bf300896cf2d3604fa60ffaaf5cbc277de7de/pathy-0.4.0-py3-none-any.whl\n",
            "Requirement already satisfied, skipping upgrade: numpy>=1.15.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (1.19.5)\n",
            "Requirement already satisfied, skipping upgrade: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy) (3.0.5)\n",
            "Collecting catalogue<2.1.0,>=2.0.1\n",
            "  Downloading https://files.pythonhosted.org/packages/de/68/027c9f70a58fa4d76521d94237e305247fca196d374635b339401ebed5d8/catalogue-2.0.2-py3-none-any.whl\n",
            "Requirement already satisfied, skipping upgrade: importlib-metadata>=0.20; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from spacy) (3.10.0)\n",
            "Requirement already satisfied, skipping upgrade: jinja2 in /usr/local/lib/python3.7/dist-packages (from spacy) (2.11.3)\n",
            "Collecting spacy-legacy<3.1.0,>=3.0.0\n",
            "  Downloading https://files.pythonhosted.org/packages/78/d8/e25bc7f99877de34def57d36769f0cce4e895b374cdc766718efc724f9ac/spacy_legacy-3.0.2-py2.py3-none-any.whl\n",
            "Collecting typer<0.4.0,>=0.3.0\n",
            "  Downloading https://files.pythonhosted.org/packages/90/34/d138832f6945432c638f32137e6c79a3b682f06a63c488dcfaca6b166c64/typer-0.3.2-py3-none-any.whl\n",
            "Requirement already satisfied, skipping upgrade: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (4.41.1)\n",
            "Requirement already satisfied, skipping upgrade: setuptools in /usr/local/lib/python3.7/dist-packages (from spacy) (54.2.0)\n",
            "Requirement already satisfied, skipping upgrade: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (20.9)\n",
            "Requirement already satisfied, skipping upgrade: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.7/dist-packages (from spacy) (1.0.5)\n",
            "Requirement already satisfied, skipping upgrade: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (1.24.3)\n",
            "Requirement already satisfied, skipping upgrade: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (2.10)\n",
            "Requirement already satisfied, skipping upgrade: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (2020.12.5)\n",
            "Requirement already satisfied, skipping upgrade: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy) (3.0.4)\n",
            "Collecting smart-open<4.0.0,>=2.2.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/11/9a/ba2d5f67f25e8d5bbf2fcec7a99b1e38428e83cb715f64dd179ca43a11bb/smart_open-3.0.0.tar.gz (113kB)\n",
            "\u001b[K     |████████████████████████████████| 122kB 44.9MB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->spacy) (3.4.1)\n",
            "Requirement already satisfied, skipping upgrade: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2->spacy) (1.1.1)\n",
            "Requirement already satisfied, skipping upgrade: click<7.2.0,>=7.1.1 in /usr/local/lib/python3.7/dist-packages (from typer<0.4.0,>=0.3.0->spacy) (7.1.2)\n",
            "Requirement already satisfied, skipping upgrade: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->spacy) (2.4.7)\n",
            "Building wheels for collected packages: smart-open\n",
            "  Building wheel for smart-open (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for smart-open: filename=smart_open-3.0.0-cp37-none-any.whl size=107098 sha256=a6565e8ffbae1c32fb8e19be74ff6bacc89c3bb0a2651dcbb16eceaf40e2d447\n",
            "  Stored in directory: /root/.cache/pip/wheels/18/88/7c/f06dabd5e9cabe02d2269167bcacbbf9b47d0c0ff7d6ebcb78\n",
            "Successfully built smart-open\n",
            "\u001b[31mERROR: catalogue 2.0.2 has requirement importlib-metadata<3.3.0,>=0.20; python_version < \"3.8\", but you'll have importlib-metadata 3.10.0 which is incompatible.\u001b[0m\n",
            "Installing collected packages: catalogue, srsly, pydantic, thinc, smart-open, typer, pathy, spacy-legacy, spacy\n",
            "  Found existing installation: catalogue 1.0.0\n",
            "    Uninstalling catalogue-1.0.0:\n",
            "      Successfully uninstalled catalogue-1.0.0\n",
            "  Found existing installation: srsly 1.0.5\n",
            "    Uninstalling srsly-1.0.5:\n",
            "      Successfully uninstalled srsly-1.0.5\n",
            "  Found existing installation: thinc 7.4.0\n",
            "    Uninstalling thinc-7.4.0:\n",
            "      Successfully uninstalled thinc-7.4.0\n",
            "  Found existing installation: smart-open 5.0.0\n",
            "    Uninstalling smart-open-5.0.0:\n",
            "      Successfully uninstalled smart-open-5.0.0\n",
            "  Found existing installation: spacy 2.2.4\n",
            "    Uninstalling spacy-2.2.4:\n",
            "      Successfully uninstalled spacy-2.2.4\n",
            "Successfully installed catalogue-2.0.2 pathy-0.4.0 pydantic-1.7.3 smart-open-3.0.0 spacy-3.0.5 spacy-legacy-3.0.2 srsly-2.4.1 thinc-8.0.2 typer-0.3.2\n",
            "2021-04-15 07:10:13.771698: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library libcudart.so.11.0\n",
            "Collecting en-core-web-lg==3.0.0\n",
            "\u001b[?25l  Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_lg-3.0.0/en_core_web_lg-3.0.0-py3-none-any.whl (778.8MB)\n",
            "\u001b[K     |████████████████████████████████| 778.8MB 23kB/s \n",
            "\u001b[?25hRequirement already satisfied: spacy<3.1.0,>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from en-core-web-lg==3.0.0) (3.0.5)\n",
            "Requirement already satisfied: pathy>=0.3.5 in /usr/local/lib/python3.7/dist-packages (from spacy<3.1.0,>=3.0.0->en-core-web-lg==3.0.0) (0.4.0)\n",
            "Requirement already satisfied: typer<0.4.0,>=0.3.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.1.0,>=3.0.0->en-core-web-lg==3.0.0) (0.3.2)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from spacy<3.1.0,>=3.0.0->en-core-web-lg==3.0.0) (54.2.0)\n",
            "Requirement already satisfied: catalogue<2.1.0,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from spacy<3.1.0,>=3.0.0->en-core-web-lg==3.0.0) (2.0.2)\n",
            "Requirement already satisfied: pydantic<1.8.0,>=1.7.1 in /usr/local/lib/python3.7/dist-packages (from spacy<3.1.0,>=3.0.0->en-core-web-lg==3.0.0) (1.7.3)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy<3.1.0,>=3.0.0->en-core-web-lg==3.0.0) (3.0.5)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.1.0,>=3.0.0->en-core-web-lg==3.0.0) (1.0.5)\n",
            "Requirement already satisfied: blis<0.8.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.1.0,>=3.0.0->en-core-web-lg==3.0.0) (0.4.1)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.1.0,>=3.0.0->en-core-web-lg==3.0.0) (4.41.1)\n",
            "Requirement already satisfied: typing-extensions<4.0.0.0,>=3.7.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from spacy<3.1.0,>=3.0.0->en-core-web-lg==3.0.0) (3.7.4.3)\n",
            "Requirement already satisfied: thinc<8.1.0,>=8.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy<3.1.0,>=3.0.0->en-core-web-lg==3.0.0) (8.0.2)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy<3.1.0,>=3.0.0->en-core-web-lg==3.0.0) (2.0.5)\n",
            "Requirement already satisfied: importlib-metadata>=0.20; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from spacy<3.1.0,>=3.0.0->en-core-web-lg==3.0.0) (3.10.0)\n",
            "Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.1.0,>=3.0.0->en-core-web-lg==3.0.0) (3.0.2)\n",
            "Requirement already satisfied: wasabi<1.1.0,>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from spacy<3.1.0,>=3.0.0->en-core-web-lg==3.0.0) (0.8.2)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.1.0,>=3.0.0->en-core-web-lg==3.0.0) (2.23.0)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.7/dist-packages (from spacy<3.1.0,>=3.0.0->en-core-web-lg==3.0.0) (2.11.3)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.1.0,>=3.0.0->en-core-web-lg==3.0.0) (20.9)\n",
            "Requirement already satisfied: srsly<3.0.0,>=2.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.1.0,>=3.0.0->en-core-web-lg==3.0.0) (2.4.1)\n",
            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.1.0,>=3.0.0->en-core-web-lg==3.0.0) (1.19.5)\n",
            "Requirement already satisfied: smart-open<4.0.0,>=2.2.0 in /usr/local/lib/python3.7/dist-packages (from pathy>=0.3.5->spacy<3.1.0,>=3.0.0->en-core-web-lg==3.0.0) (3.0.0)\n",
            "Requirement already satisfied: click<7.2.0,>=7.1.1 in /usr/local/lib/python3.7/dist-packages (from typer<0.4.0,>=0.3.0->spacy<3.1.0,>=3.0.0->en-core-web-lg==3.0.0) (7.1.2)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->spacy<3.1.0,>=3.0.0->en-core-web-lg==3.0.0) (3.4.1)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.1.0,>=3.0.0->en-core-web-lg==3.0.0) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.1.0,>=3.0.0->en-core-web-lg==3.0.0) (2020.12.5)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.1.0,>=3.0.0->en-core-web-lg==3.0.0) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy<3.1.0,>=3.0.0->en-core-web-lg==3.0.0) (3.0.4)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2->spacy<3.1.0,>=3.0.0->en-core-web-lg==3.0.0) (1.1.1)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=20.0->spacy<3.1.0,>=3.0.0->en-core-web-lg==3.0.0) (2.4.7)\n",
            "Installing collected packages: en-core-web-lg\n",
            "Successfully installed en-core-web-lg-3.0.0\n",
            "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the package via spacy.load('en_core_web_lg')\n",
            "Collecting jsonlines\n",
            "  Downloading https://files.pythonhosted.org/packages/d4/58/06f430ff7607a2929f80f07bfd820acbc508a4e977542fefcc522cde9dff/jsonlines-2.0.0-py3-none-any.whl\n",
            "Installing collected packages: jsonlines\n",
            "Successfully installed jsonlines-2.0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tusA8zf8BGP9"
      },
      "source": [
        "Loading  Data from a Demo of 100 Summaries. \n",
        "https://physionet.org/content/mimiciii-demo/1.4/\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JNSzaC_vPDzD"
      },
      "source": [
        "import jsonlines as js\n",
        "book = []\n",
        "with js.open(\"notes.ndjson\") as reader:\n",
        "  num = 0\n",
        "  for summary in reader:\n",
        "    if num<100:\n",
        "        book.append(summary[2])\n",
        "        num = num + 1\n",
        "\n",
        "clinical_notes = [\" \".join( sum(book[i],[])) for i in range(100)]"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iSBTkG3eUe6C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "644ae4a4-d4d3-43ae-e958-53c1d3badc2e"
      },
      "source": [
        "# Total Discharge summaries we have for establishing the experiment\n",
        "print(len(clinical_notes))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "100\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iwcYS-PjAzA4"
      },
      "source": [
        "#Evaluation Criteria Based on probabilty distribution sripada et al .\n",
        "from scipy.stats import entropy\n",
        "from scipy.spatial import distance\n",
        "\n",
        "from collections import Counter\n",
        "from collections import OrderedDict\n",
        "\n",
        "def kld(summ, org): \n",
        "        dist_original=Counter(org.lower().split())\n",
        "        dist_summary=Counter(summ.lower().split())\n",
        "        q = list(dist_original.values())\n",
        "        p=list(dist_summary.values())\n",
        "        a=min(len(p),len(q))\n",
        "        return entropy(p[0:a],qk=q[0:a])\n",
        " \n",
        "def jsd(summ,org):\n",
        "        dist_original=Counter(org.lower().split())\n",
        "        dist_summary=Counter(summ.lower().split())\n",
        "        p = list(dist_original.values())\n",
        "        q = list(dist_summary.values())\n",
        "        a=min(len(p),len(q))\n",
        "        return distance.jensenshannon(p[0:a],q[0:a])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o9AKnSkFArGb"
      },
      "source": [
        "#Frequency Based Summarizer\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ic1txRFcHJiK"
      },
      "source": [
        "import spacy\n",
        "from collections import Counter\n",
        "from string import punctuation\n",
        "import en_core_web_lg\n",
        "nlp = en_core_web_lg.load()\n",
        "# nlp = spacy.load(\"en_core_web_lg\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gpV5VPDu9ZhA"
      },
      "source": [
        "# Function using POS summarization based on idea similar to Edmundson et al\n",
        "def summarize(text):\n",
        "    keyword = []\n",
        "    pos_tag = ['PROPN', 'ADJ', 'NOUN', 'VERB']\n",
        "    doc = nlp(text.lower())\n",
        "    for token in doc:\n",
        "        if(token.text in nlp.Defaults.stop_words or token.text in punctuation):\n",
        "            continue\n",
        "        if(token.pos_ in pos_tag):\n",
        "            keyword.append(token.text)\n",
        "    \n",
        "    freq_word = Counter(keyword)\n",
        "    max_freq = Counter(keyword).most_common(1)[0][1]\n",
        "    for w in freq_word:\n",
        "        freq_word[w] = (freq_word[w]/max_freq)\n",
        "        \n",
        "    sent_strength={}\n",
        "    for sent in doc.sents:\n",
        "        for word in sent:\n",
        "            if word.text in freq_word.keys():\n",
        "                if sent in sent_strength.keys():\n",
        "                    sent_strength[sent]+=freq_word[word.text]\n",
        "                else:\n",
        "                    sent_strength[sent]=freq_word[word.text] \n",
        "    summary = []\n",
        "    \n",
        "    sorted_x = sorted(sent_strength.items(), key=lambda kv: kv[1], reverse=True)\n",
        "    limit=len(sorted_x)/15\n",
        "    counter = 0\n",
        "    for i in range(len(sorted_x)):\n",
        "        summary.append(str(sorted_x[i][0]).capitalize())\n",
        "\n",
        "        counter += 1\n",
        "        if(counter >= limit):\n",
        "            break\n",
        "            \n",
        "    return ' '.join(summary)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HMhotlbgOjJ1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "6922c555-4f2c-4f6e-df06-50384b1fea74"
      },
      "source": [
        "summary=[summarize(note) for note in clinical_notes]\n",
        "summary[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Disp tablet s refills ranitidine hcl mg tablet sig one tablet po daily daily disp tablet s refills tramadol mg tablet sig Tablet sig two tablet po q6h every hours as needed for pain tablet s senna mg tablet sig One tablet po daily daily disp tablet s refills furosemide mg tablet sig Mitral valve repair coronary artery bypass graft x left internal mammary artery to left anterior descending history of present illness year old female who was told she had mvp since age currently quite active but has noticed some dyspnea on exertion when walking up hills most recent echo revealed severe mvp and moderate to severe Daily daily disp tablet delayed release e.c. s refills docusate sodium mg capsule sig one capsule po bid times Disp tablet er particles crystals s refills discharge disposition home with service facility hospitaltoken vna discharge diagnosis mitral regurgitation coronary artery artery disease s Aspirin mg tablet delayed release e.c. sig one tablet delayed release e.c. po'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ubXa8xFAi-QN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "584de5cb-56d0-4a0f-d1e4-5974c376cf4b"
      },
      "source": [
        "len(summary[0])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "989"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LAqwg0FMi_5u",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "146ed3b4-13b3-47da-ce02-2aa16a9da0ac"
      },
      "source": [
        "from collections import Counter\n",
        "from collections import OrderedDict\n",
        "#  forming a frequency distribution for words\n",
        "dist_original=Counter(clinical_notes[0].lower().split())\n",
        "dis_summary=Counter(summary[0].lower().split())\n",
        "\n",
        "print(list(dist_original.values()))\n",
        "print(list(dis_summary.values()))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[3, 3, 12, 7, 5, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 1, 1, 2, 16, 2, 3, 1, 1, 3, 8, 2, 1, 8, 5, 3, 4, 6, 3, 3, 17, 11, 1, 1, 15, 1, 2, 5, 1, 1, 1, 1, 1, 1, 12, 1, 12, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 4, 1, 1, 1, 3, 1, 3, 16, 3, 3, 1, 18, 1, 2, 2, 4, 2, 2, 2, 2, 5, 12, 2, 2, 5, 2, 4, 2, 2, 2, 2, 2, 11, 4, 2, 2, 6, 1, 1, 1, 2, 2, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 8, 1, 1, 1, 1, 1, 1, 1, 1, 11, 1, 1, 1, 1, 1, 1, 1, 1, 6, 1, 1, 2, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 3, 1, 1, 1, 2, 1, 11, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 4, 1, 2, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 2, 1, 1, 15, 3, 18, 1, 1, 7, 1, 2, 14, 1, 1, 1, 2, 1, 3, 1, 1, 1, 1, 1, 4, 1, 1, 1, 2, 2, 2, 1, 2, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 6, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 15, 3, 1, 1, 1, 1, 4, 1, 8, 1, 1, 1, 2, 3, 1, 1, 1, 1, 1, 1, 1, 1, 8, 1, 1, 1, 2, 2, 1, 1, 1, 2, 1, 5, 1, 1, 1, 1, 1, 1, 2, 2, 1, 5, 1, 2, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 2, 2, 1, 2, 1, 1, 2, 2, 4, 1, 1, 1, 5, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 2, 3, 1, 2, 1, 4, 1, 1, 1, 1, 2, 1, 1, 2, 1, 2, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 1, 25, 3, 3, 3, 11, 8, 11, 8, 7, 7, 1, 2, 2, 3, 1, 1, 2, 1, 1, 1, 1, 3, 1, 1, 1, 2, 2, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 3, 1, 3, 1, 1, 1, 1, 1, 1, 3, 2, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 1, 1, 2, 6, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 2, 2, 2, 2, 2, 2, 1, 1, 3, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
            "[5, 16, 7, 5, 1, 1, 6, 7, 4, 5, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 2, 4, 1, 1, 1, 2, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 3, 3, 3, 1, 1, 2, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-foEH5t0mXXE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "7da88d70-68e9-4253-c366-f67a2f7d8633"
      },
      "source": [
        "kld_freq=[kld(clinical_notes[i],summary[i]) for i in range(100)]\n",
        "print(kld_freq)\n",
        "jsd_freq=[jsd(summ,org) for org,summ in zip(clinical_notes,summary)]\n",
        "print(jsd_freq)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.7199990163091441, 1.288577094003375, 0.6876781653361953, 1.0548275305953054, 0.822106467044532, 1.0250455035798383, 0.7642230697261388, 0.8861677097328216, 0.8133151448774971, 1.070300835591361, 0.8902506584557677, 1.0409563178920767, 0.9325518447723371, 0.876780478796326, 0.7527658673801978, 1.109862192707198, 0.9814609731836896, 1.3140823138880307, 0.6336290837851819, 0.5945883813485552, 0.9853710957839535, 0.6425021501485686, 0.7927687220316615, 1.0248072650693685, 0.7909853199013283, 0.917756720574602, 0.7840406497243342, 0.36351808643481, 0.7646289844561274, 0.7901441270581637, 1.3074442360423464, 1.1585353169123405, 0.8390222886295955, 0.7277708334637526, 0.9440759880626365, 1.0119889895584742, 0.4404568982928508, 0.809263712380167, 0.9257424674505709, 0.8088068380486474, 1.1703079980729523, 1.0009809316501646, 0.6645238775763517, 0.9657224564406461, 0.8211600933863579, 0.9475945948239577, 1.0045711656352914, 0.8222920904678139, 0.9173348150335361, 1.0650339501847848, 0.9150650487002507, 0.8592161332260143, 0.7760992513052745, 0.9899705820711331, 0.25402090348903744, 1.0738141312402973, 0.958779532547603, 0.8047759506636604, 0.8837316319163978, 1.0237833505131568, 1.0448370196047883, 0.7598523168375311, 0.7421577154953737, 0.7060657854315938, 0.9310896222377714, 0.7222608882096487, 1.0117940181455518, 1.235252849808724, 0.7749524468673616, 0.6028963762294497, 0.8497471330550408, 1.188247565159837, 1.1064630436808665, 0.8005052123107964, 0.943394967833588, 0.8352263506528074, 1.261395619257123, 0.9057660069747315, 0.7437664404687891, 0.8961443246169718, 0.992520711484797, 0.9310731755674274, 0.8276231800837823, 0.8482506953980145, 0.951658753833869, 0.6529169649183043, 0.6827022343894, 0.8240654539174791, 1.3381892931093735, 0.8883881871861411, 1.3539373370730008, 0.7983410280939688, 0.7374364832394635, 0.841450466192277, 0.7601875735059431, 0.49707873253014384, 0.8610639835107452, 1.0040131802807752, 1.244057337071036, 1.1587067148922152]\n",
            "[0.39720931225497386, 0.4852511366314636, 0.3872706804927176, 0.46340325681824046, 0.4099997986976341, 0.46005880733558646, 0.4002969489882648, 0.44234415036007135, 0.421925310099122, 0.4693768278740044, 0.4158760771563454, 0.45895034411578084, 0.4402055982830778, 0.41583161778047406, 0.3969658596406603, 0.4911221113095487, 0.445479559429233, 0.5046759063512024, 0.3713131621866397, 0.3478467357335101, 0.4340713440256459, 0.3749889088313121, 0.41151153011543545, 0.4588922511707417, 0.4091819540392327, 0.43419640905267, 0.4065440662077568, 0.2844284268589189, 0.40559652794340256, 0.4014378102932751, 0.4967820570737719, 0.4870344672921085, 0.4078101320044515, 0.4108891690505209, 0.43240798520967044, 0.44935119240437205, 0.30652719506018367, 0.4162684644669471, 0.4354268177375234, 0.39773859743967266, 0.4715521382530961, 0.447962003273332, 0.38610627249095786, 0.4315213182615697, 0.41408626881185323, 0.44327199859245164, 0.44658865113350443, 0.40915999634888267, 0.432579406441209, 0.46516287510699234, 0.43411307155006607, 0.4231658229357147, 0.3998127104906795, 0.45057579185007557, 0.2290297743715948, 0.4760834031030112, 0.4335523857629184, 0.41516752657671646, 0.43874027686937406, 0.4568945731781437, 0.47081245798217464, 0.4056472661746331, 0.3979407366977592, 0.37717446499159896, 0.4340829736581772, 0.3881235315133434, 0.44593482069341067, 0.4900066637617449, 0.4013769642616592, 0.3620793700689522, 0.42967583582364666, 0.5092855744997147, 0.4815715537389554, 0.4107153861102546, 0.4324135002174301, 0.41260020432057676, 0.47584168236171737, 0.45241197157420976, 0.39985777827917024, 0.44433796189167596, 0.4507521245052968, 0.43070964328348293, 0.4142477213322459, 0.4282527282335757, 0.4372838180953955, 0.37491735876279997, 0.3816171949760131, 0.42752970547470326, 0.4951776614304662, 0.43596184440374197, 0.5136152666821995, 0.41795562355788785, 0.4099793980911211, 0.4202847642635409, 0.4098869056748805, 0.32936378459450627, 0.4188613183589922, 0.4511300428259509, 0.48116143642937387, 0.4832579831809677]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GacXvRQ7lA0X",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "3efcee89-e6c3-49b8-bf83-b17c7a3ec4d5"
      },
      "source": [
        "import statistics as sc\n",
        "print(sc.mean(kld_freq))\n",
        "print(sc.mean(jsd_freq))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8926305501713105\n",
            "0.42625451795996255\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CbO6iU2LcTDU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 887
        },
        "outputId": "accf1a19-35e7-4383-a673-ef162d719094"
      },
      "source": [
        "!pip install sumy\n",
        "!pip install lexrank"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting sumy\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/61/20/8abf92617ec80a2ebaec8dc1646a790fc9656a4a4377ddb9f0cc90bc9326/sumy-0.8.1-py2.py3-none-any.whl (83kB)\n",
            "\r\u001b[K     |████                            | 10kB 10.8MB/s eta 0:00:01\r\u001b[K     |███████▉                        | 20kB 1.7MB/s eta 0:00:01\r\u001b[K     |███████████▊                    | 30kB 2.2MB/s eta 0:00:01\r\u001b[K     |███████████████▋                | 40kB 2.5MB/s eta 0:00:01\r\u001b[K     |███████████████████▌            | 51kB 2.0MB/s eta 0:00:01\r\u001b[K     |███████████████████████▍        | 61kB 2.3MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▍    | 71kB 2.5MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▎| 81kB 2.7MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 92kB 2.5MB/s \n",
            "\u001b[?25hRequirement already satisfied: docopt<0.7,>=0.6.1 in /usr/local/lib/python3.6/dist-packages (from sumy) (0.6.2)\n",
            "Requirement already satisfied: nltk>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from sumy) (3.2.5)\n",
            "Collecting breadability>=0.1.20\n",
            "  Downloading https://files.pythonhosted.org/packages/ad/2d/bb6c9b381e6b6a432aa2ffa8f4afdb2204f1ff97cfcc0766a5b7683fec43/breadability-0.1.20.tar.gz\n",
            "Collecting pycountry>=18.2.23\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/16/b6/154fe93072051d8ce7bf197690957b6d0ac9a21d51c9a1d05bd7c6fdb16f/pycountry-19.8.18.tar.gz (10.0MB)\n",
            "\u001b[K     |████████████████████████████████| 10.0MB 7.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests>=2.7.0 in /usr/local/lib/python3.6/dist-packages (from sumy) (2.23.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from nltk>=3.0.2->sumy) (1.12.0)\n",
            "Requirement already satisfied: chardet in /usr/local/lib/python3.6/dist-packages (from breadability>=0.1.20->sumy) (3.0.4)\n",
            "Requirement already satisfied: lxml>=2.0 in /usr/local/lib/python3.6/dist-packages (from breadability>=0.1.20->sumy) (4.2.6)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.7.0->sumy) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.7.0->sumy) (2.9)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.7.0->sumy) (2020.4.5.1)\n",
            "Building wheels for collected packages: breadability, pycountry\n",
            "  Building wheel for breadability (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for breadability: filename=breadability-0.1.20-py2.py3-none-any.whl size=21684 sha256=be59f5d3c7597a048bd9a93cd0b6285133054392fc90bf6db565f141f2eeab0c\n",
            "  Stored in directory: /root/.cache/pip/wheels/5a/4d/a1/510b12c5e65e0b2b3ce539b2af66da0fc57571e528924f4a52\n",
            "  Building wheel for pycountry (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pycountry: filename=pycountry-19.8.18-py2.py3-none-any.whl size=10627361 sha256=7afccf72882b0b44c7fe6757b187fd8881449ff5ee490e4fe8805b20ec52bca8\n",
            "  Stored in directory: /root/.cache/pip/wheels/a2/98/bf/f0fa1c6bf8cf2cbdb750d583f84be51c2cd8272460b8b36bd3\n",
            "Successfully built breadability pycountry\n",
            "Installing collected packages: breadability, pycountry, sumy\n",
            "Successfully installed breadability-0.1.20 pycountry-19.8.18 sumy-0.8.1\n",
            "Collecting lexrank\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/e1/25/f139d8526e014b6bf6021305492cd7ccffbfa10999802fce4813808b04e4/lexrank-0.1.0-py3-none-any.whl (69kB)\n",
            "\u001b[K     |████████████████████████████████| 71kB 2.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: scipy>=0.19.0 in /usr/local/lib/python3.6/dist-packages (from lexrank) (1.4.1)\n",
            "Collecting path.py>=10.5\n",
            "  Downloading https://files.pythonhosted.org/packages/a5/0d/4caee829b04e3113b7069fa52063bce5c78e374e05850aa893549e917a1a/path.py-12.4.0-py3-none-any.whl\n",
            "Requirement already satisfied: regex>=2017.11.9 in /usr/local/lib/python3.6/dist-packages (from lexrank) (2019.12.20)\n",
            "Requirement already satisfied: pyrsistent>=0.14.0 in /usr/local/lib/python3.6/dist-packages (from lexrank) (0.16.0)\n",
            "Collecting urlextract>=0.7\n",
            "  Downloading https://files.pythonhosted.org/packages/06/db/23b47f32d990dea1d9852ace16d551a0003bdfc8be33094cfd208757466e/urlextract-0.14.0-py3-none-any.whl\n",
            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.6/dist-packages (from lexrank) (1.18.4)\n",
            "Collecting path<13.2\n",
            "  Downloading https://files.pythonhosted.org/packages/4d/24/5827e075036b5bb6b538f71bf39574d4a8024c5df51206cb9d6739e24d94/path-13.1.0-py3-none-any.whl\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from pyrsistent>=0.14.0->lexrank) (1.12.0)\n",
            "Collecting uritools\n",
            "  Downloading https://files.pythonhosted.org/packages/eb/1a/5995c0a000ef116111b9af9303349ba97ec2446d2c9a79d2df028a3e3b19/uritools-3.0.0-py3-none-any.whl\n",
            "Requirement already satisfied: idna in /usr/local/lib/python3.6/dist-packages (from urlextract>=0.7->lexrank) (2.9)\n",
            "Collecting appdirs\n",
            "  Downloading https://files.pythonhosted.org/packages/56/eb/810e700ed1349edde4cbdc1b2a21e28cdf115f9faf263f6bbf8447c1abf3/appdirs-1.4.3-py2.py3-none-any.whl\n",
            "Requirement already satisfied: importlib-metadata>=0.5; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from path<13.2->path.py>=10.5->lexrank) (1.6.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata>=0.5; python_version < \"3.8\"->path<13.2->path.py>=10.5->lexrank) (3.1.0)\n",
            "Installing collected packages: path, path.py, uritools, appdirs, urlextract, lexrank\n",
            "Successfully installed appdirs-1.4.3 lexrank-0.1.0 path-13.1.0 path.py-12.4.0 uritools-3.0.0 urlextract-0.14.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qed_L4-Vb_fd"
      },
      "source": [
        "Algorithm\n",
        "It uses greedy optimization approach:\n",
        "\n",
        "Set S={} and d=0\n",
        "while |S|<= L do:\n",
        "for i in [1...ND] , di=KL(Ps||PD)\n",
        "Set S=S + Si with minimum di and d=di\n",
        "Stop if there is no i such that di:<d"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xpqei5pxb-h8"
      },
      "source": [
        "# from sumy.summarizers.kl import KLSummarizer\n",
        "# from sumy.summarizers import AbstractSummarizer"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Toel7PTOgyN4"
      },
      "source": [
        "#Graph Based Summarization\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HrQS7Jnqg0l4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "08875255-27b3-427f-9572-17ae1d12fa72"
      },
      "source": [
        "from sumy.summarizers.text_rank import TextRankSummarizer\n",
        "from sumy.summarizers.lex_rank import LexRankSummarizer \n",
        "from sumy.nlp.tokenizers import Tokenizer\n",
        "from sumy.parsers.plaintext import PlaintextParser\n",
        "import nltk\n",
        "nltk.download('punkt')\n",
        "\n",
        "# parser=PlaintextParser.from_string(clinical_notes,Tokenizer(\"english\"))\n",
        "summarizer_g = TextRankSummarizer()\n",
        "summarizer_l = LexRankSummarizer()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ModuleNotFoundError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-15-e179b3d401b5>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0msumy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummarizers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtext_rank\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mTextRankSummarizer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msumy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummarizers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlex_rank\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mLexRankSummarizer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msumy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtokenizers\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mTokenizer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msumy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparsers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplaintext\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mPlaintextParser\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mnltk\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'sumy'",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "74O8XI7d9PVZ"
      },
      "source": [
        "def graph_summarizer(doc):\n",
        "      parser=PlaintextParser.from_string(doc,Tokenizer(\"english\"))\n",
        "      # return summarizer_g(parser.document,5)\n",
        "      return summarizer_l(parser.document,2)\n"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jpTIBRUT99I6"
      },
      "source": [
        "summary_graph = [graph_summarizer(summ) for summ in clinical_notes]\n",
        "for sentence in summary_graph[0]:\n",
        "    print(sentence , end= \" \")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v9IW0IGzL2vj"
      },
      "source": [
        "final_summ = [None]*100\n",
        "for i in range(100):\n",
        "  final_summ[i] = \"\".join(str(list(summary_graph[i])))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nuVXQAigS_TX"
      },
      "source": [
        "final_summ[0]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "86EaF7KH7e8C"
      },
      "source": [
        "kld_graph=[kld(final_summ[i],clinical_notes[i]) for i in range(100)]\n",
        "print(kld_graph)\n",
        "jsd_graph=[jsd(summ,org) for org,summ in zip(clinical_notes,final_summ)]\n",
        "print(jsd_graph)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xjYqoebgTVRI"
      },
      "source": [
        "import statistics as sc\n",
        "print(sc.mean(kld_graph))\n",
        "print(sc.mean(jsd_graph))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dzn0-4uGVc0e"
      },
      "source": [
        "#BERT Extractive Summarizer\n",
        "\n",
        "This works by first embedding the sentences, then running a clustering algorithm, finding the sentences that are closest to the cluster's centroids. This library also uses coreference techniques, utilizing the https://github.com/huggingface/neuralcoref library to resolve words in summaries that need more context."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4qkLWZ_QaEDG",
        "outputId": "682490d8-031b-4796-af6e-9f2a21efc9be",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!pip install bert-extractive-summarizer\n",
        "!pip install neuralcoref\n",
        "!pip install spacy"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting bert-extractive-summarizer\n",
            "  Downloading https://files.pythonhosted.org/packages/1a/07/fdb05f9e18b6f641499ef56737126fbd2fafe1cdc1a04ba069d5aa205901/bert_extractive_summarizer-0.7.1-py3-none-any.whl\n",
            "Collecting transformers\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d8/b2/57495b5309f09fa501866e225c84532d1fd89536ea62406b2181933fb418/transformers-4.5.1-py3-none-any.whl (2.1MB)\n",
            "\u001b[K     |████████████████████████████████| 2.1MB 4.3MB/s \n",
            "\u001b[?25hRequirement already satisfied: spacy in /usr/local/lib/python3.7/dist-packages (from bert-extractive-summarizer) (3.0.5)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from bert-extractive-summarizer) (0.22.2.post1)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from transformers->bert-extractive-summarizer) (20.9)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers->bert-extractive-summarizer) (1.19.5)\n",
            "Collecting sacremoses\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/08/cd/342e584ee544d044fb573ae697404ce22ede086c9e87ce5960772084cad0/sacremoses-0.0.44.tar.gz (862kB)\n",
            "\u001b[K     |████████████████████████████████| 870kB 12.6MB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers->bert-extractive-summarizer) (4.41.1)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers->bert-extractive-summarizer) (2.23.0)\n",
            "Requirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from transformers->bert-extractive-summarizer) (3.10.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers->bert-extractive-summarizer) (2019.12.20)\n",
            "Collecting tokenizers<0.11,>=0.10.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ae/04/5b870f26a858552025a62f1649c20d29d2672c02ff3c3fb4c688ca46467a/tokenizers-0.10.2-cp37-cp37m-manylinux2010_x86_64.whl (3.3MB)\n",
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            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2->spacy->bert-extractive-summarizer) (1.1.1)\n",
            "Building wheels for collected packages: sacremoses\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.44-cp37-none-any.whl size=886084 sha256=6efef40d249fec83b4fe02aa07a188b5d564982bd7411c3c8949dc95a2bdca27\n",
            "  Stored in directory: /root/.cache/pip/wheels/3e/fb/c0/13ab4d63d537658f448366744654323077c4d90069b6512f3c\n",
            "Successfully built sacremoses\n",
            "Installing collected packages: sacremoses, tokenizers, transformers, bert-extractive-summarizer\n",
            "Successfully installed bert-extractive-summarizer-0.7.1 sacremoses-0.0.44 tokenizers-0.10.2 transformers-4.5.1\n",
            "Collecting neuralcoref\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/06/6d/c90e5bfd1b8ef32f1b231a32f2f625bf33df7525324d2bbcd08992791d64/neuralcoref-4.0-cp37-cp37m-manylinux1_x86_64.whl (286kB)\n",
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            "Collecting boto3\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/aa/98/e9459d65ad8ab27886bf9d86a537e6f65b5bdbc4c7de68ba45b524ef74a1/boto3-1.17.52-py2.py3-none-any.whl (131kB)\n",
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            "Collecting s3transfer<0.4.0,>=0.3.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/00/89/0cb4e92c239e6425b9b0035227b8cdf9d3d098a5c9e95632c3815df63a09/s3transfer-0.3.7-py2.py3-none-any.whl (73kB)\n",
            "\u001b[K     |████████████████████████████████| 81kB 5.2MB/s \n",
            "\u001b[?25hCollecting botocore<1.21.0,>=1.20.52\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/8f/f3/be57d6b74618db08bcc67be52da014c8d12132fedb1d12457c386b7be373/botocore-1.20.52-py2.py3-none-any.whl (7.4MB)\n",
            "\u001b[K     |████████████████████████████████| 7.4MB 6.7MB/s \n",
            "\u001b[?25hCollecting jmespath<1.0.0,>=0.7.1\n",
            "  Downloading https://files.pythonhosted.org/packages/07/cb/5f001272b6faeb23c1c9e0acc04d48eaaf5c862c17709d20e3469c6e0139/jmespath-0.10.0-py2.py3-none-any.whl\n",
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            "\u001b[31mERROR: botocore 1.20.52 has requirement urllib3<1.27,>=1.25.4, but you'll have urllib3 1.24.3 which is incompatible.\u001b[0m\n",
            "Installing collected packages: jmespath, botocore, s3transfer, boto3, neuralcoref\n",
            "Successfully installed boto3-1.17.52 botocore-1.20.52 jmespath-0.10.0 neuralcoref-4.0 s3transfer-0.3.7\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VvAJPDkQMTmt",
        "outputId": "26c8b949-1e80-4270-cd0e-ce0977594dc5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "pip install sentencepiece"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting sentencepiece\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f5/99/e0808cb947ba10f575839c43e8fafc9cc44e4a7a2c8f79c60db48220a577/sentencepiece-0.1.95-cp37-cp37m-manylinux2014_x86_64.whl (1.2MB)\n",
            "\u001b[K     |████████████████████████████████| 1.2MB 3.6MB/s \n",
            "\u001b[?25hInstalling collected packages: sentencepiece\n",
            "Successfully installed sentencepiece-0.1.95\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zyQN1uloaOlR",
        "outputId": "aa269ea8-c184-4e58-ceee-5ba18941e5d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261,
          "referenced_widgets": [
            "8a0c56b9f15f4b9c8069939aea4b1989",
            "02f45a2600034ab2a30e140e45d118c5",
            "a913eb61e93d46878dfcaeebfb1ee7a6",
            "23ce4392c66c413ba4346e0d9bab8d0a",
            "64ce2e2fac40413192e8d1b4b14dd377",
            "60b8305021a24b5c9864842a13157064",
            "000f3208a907479391a873e8da82e44d",
            "70d612aec88b44099f53b1c714efb847",
            "8f51582122804c50882dcaa8904d199e",
            "2479fb085b7e429aa4aacf79f67b5cc6",
            "db96b8bdbfd3468791b555c5571a11ad",
            "cf54cd403b8644c9a3d0b6928e428618",
            "6cdbf3bc7cee4cd388de1ed23f66c8ff",
            "b3ac604793dc48fe9c3e932ba0566004",
            "cca7409b018049188dcf989952b89265",
            "21969e17d5a04686b08ad714c1e2cca6",
            "0734e47e85464116960227e33ad4b6f3",
            "8d36d9a52e4344be8d539d13bc6af692",
            "2148c8ee9e984cc29efcd4cf1e91bb13",
            "35ae2e47eeac417b9cb739b48c9a84eb",
            "a057422c324e4535b616d170d8283e2a",
            "5f09668bad5b45a396527c65b2af2b7e",
            "4763b6db4e9a4068a102cf157a943c96",
            "fdfe39d5113d44408c5f4eea1860f067",
            "efa93e8d6b344e788763faa937aeb634",
            "9cd90f7c39834f33b569c5b564eb7878",
            "48ae285df695419898a433156ab30dda",
            "3b35fd985bec40e3a885561691b2e35c",
            "79e16ed2bb0c4c68a0e86ff007d211e3",
            "b8779ebd049642e38805c318011a1263",
            "2b92a82fc2f34ea2b46e73c392b9b9c2",
            "8f2406b5431549b5a9503c4ab612ad9c",
            "b60a6b1af9b44a168f68684897ee4e9b",
            "43ec8e0ba3344f07937df46a4f3b168c",
            "76211383f57846e0bed7ec698368cdf2",
            "394de0f49cb443059ce40ba07b046133",
            "d2ec336241474b9a923c2cf59a75e160",
            "568d6937b8574f438158109a7b11fcba",
            "31705c0e467f4d7ead2009121c660019",
            "475d9d6c122640778303182712072860"
          ]
        }
      },
      "source": [
        "from summarizer import Summarizer\n",
        "# #handler = CoreferenceHandler(greedyness=.4)\n",
        "# model = Summarizer(sentence_handler=handler)\n",
        "model = Summarizer()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8a0c56b9f15f4b9c8069939aea4b1989",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=434.0, style=ProgressStyle(description_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "8f51582122804c50882dcaa8904d199e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1344997306.0, style=ProgressStyle(descr…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "0734e47e85464116960227e33ad4b6f3",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=231508.0, style=ProgressStyle(descripti…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "efa93e8d6b344e788763faa937aeb634",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=28.0, style=ProgressStyle(description_w…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b60a6b1af9b44a168f68684897ee4e9b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=466062.0, style=ProgressStyle(descripti…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N6wFBdykL10H"
      },
      "source": [
        "def extr_summarizer(doc):\n",
        "      result = model(doc, num_sentences=10)\n",
        "      return ''.join(result)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9q3VCwFxNJ-z",
        "outputId": "43cb1c5d-a37a-4e57-8ab2-7752204d7b9e"
      },
      "source": [
        "summary_extr = [extr_summarizer(summ) for summ in clinical_notes]"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/summarizer/cluster_features.py:149: ConvergenceWarning: Number of distinct clusters (8) found smaller than n_clusters (9). Possibly due to duplicate points in X.\n",
            "  model = self.__get_model(k).fit(self.features)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "id": "oMmGEx9eO1LG",
        "outputId": "50054688-91a2-422f-c777-1e3c91573419"
      },
      "source": [
        "summary_extr[0]"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'po daily daily disp tablet delayed release e.c. lastnametoken on february at 15pm cardiologist dr. lastnametoken on february at 30am wound check on thurs january at am with cardiac surgery on hospitaltoken please call to schedule appointments with your primary care dr. lastnametoken in march weeks please call cardiac surgery office with any questions or concerns telephonenumbertoken answering service will contact on call person during off hours completed by january'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zN4oYruDZell"
      },
      "source": [
        "#Evaluation Criteria Based on probabilty distribution hypothesis sripada et al .\n",
        "from scipy.stats import entropy\n",
        "from scipy.spatial import distance\n",
        "\n",
        "from collections import Counter\n",
        "from collections import OrderedDict\n",
        "\n",
        "def kld(summ, org): \n",
        "        dist_original=Counter(str(org).lower().split())\n",
        "        dist_summary=Counter(str(summ).lower().split())\n",
        "        q = list(dist_original.values())\n",
        "        p=list(dist_summary.values())\n",
        "        a=min(len(p),len(q))\n",
        "        return entropy(p[0:a],qk=q[0:a])\n",
        " \n",
        "def jsd(summ,org):\n",
        "        dist_original=Counter(str(org).lower().split())\n",
        "        dist_summary=Counter(str(summ).lower().split())\n",
        "        p = list(dist_original.values())\n",
        "        q = list(dist_summary.values())\n",
        "        a=min(len(p),len(q))\n",
        "        return distance.jensenshannon(p[0:a],q[0:a])"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TZmXSTT0NVeV"
      },
      "source": [
        "kld_extr = [kld(summary_extr[i],clinical_notes[i]) for i in range(100)]\n",
        "jsd_extr = [jsd(summ,org) for org,summ in zip(clinical_notes,summary_extr)]"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gzudPyqzX75n"
      },
      "source": [
        "# print(jsd_extr)"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FryNKRVMX7IX"
      },
      "source": [
        "# print(kld_extr)"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YYO5hHgZN5QG",
        "outputId": "8e3cc1e2-4c0c-495e-de13-e6c5b04f3acd"
      },
      "source": [
        "import statistics as sc\n",
        "print(sc.mean(kld_extr))\n",
        "print(sc.mean(jsd_extr))"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.8188729477123553\n",
            "0.414378066906262\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VHjF_hjw-D1R"
      },
      "source": [
        "#saving values for paper figure reproducibilty \n",
        "kld_extr = [0.6842839295529844, 0.7977338743284382, 0.5563448921486086, 0.0, 0.650041832310987, 0.9879807463328246, 0.9178073561080327, 0.793975147520496, 0.7294714408155729, 0.5436764420810272, 0.5059154749516336, 0.4551771222679759, 0.8930379909918174, 0.4812437378477629, 0.6076818543698523, 0.7742248472436721, 0.7953275087477899, 0.4080821223600874, 0.7471956269489444, 0.4674465180725232, 0.5739576815215496, 1.0849542422952438, 0.5469967807232509, 0.8057589196705631, 0.733428113620223, 0.6179537616310854, 0.42513755448109114, 0.6186836076801235, 0.661148944159633, 0.6006554535217499, 1.005374042540837, 0.9515003342323526, 1.0732822349412607, 0.7262704041759033, 1.007124181059896, 0.8467893082057485, 0.0, 1.033170157133659, 0.7113843525741337, 0.5264649543611555, 0.9375720510200491, 0.953207826523899, 0.37356080634258193, 1.073976644398436, 0.0, 0.7468974187113244, 0.9028639184522681, 0.7682308988058449, 0.7168563936767004, 0.8756798049335361, 0.0, 1.0680415870251325, 0.9566413720923004, 0.9449576623705486, 0.4903198982399414, 0.9119085469372984, 0.48542011747657676, 0.5780943425248267, 0.7896674437498101, 1.1745245943752975, 0.9399622223613009, 0.6524895427240437, 0.0, 0.7901774420491993, 0.0, 0.6025816064990642, 1.2528338077952927, 0.92743389936941, 0.8766870745676044, 0.25124713536228166, 0.8335601015770484, 0.5913920887040359, 0.5358172620797355, 0.0, 0.0, 0.0, 0.8559918760942445, 0.5795336889606747, 0.7043395000430008, 0.6403703906493141, 0.9204451065271085, 0.7067761501401544, 0.9310116322634805, 0.6925831501893108, 1.0536382620463987, 0.6890920669949064, 0.3700791602546419, 0.0, 0.0, 1.1172083011359595, 1.1432497835089457, 0.0, 0.6716732556143133, 1.0532713354695107, 0.6816544509499052, 0.0, 0.7150526267154107, 0.0, 0.8075167246497679, 0.8270613505054992]\n",
        "jsd_extr = [0.3889800200362971, 0.4150140948147177, 0.35341466221567985, 0.0, 0.3871579196253934, 0.47350681413515644, 0.45872669701548296, 0.4134906593581557, 0.37780829670084193, 0.35516831338690913, 0.3408699372839849, 0.3317326794337331, 0.4321788283693042, 0.32849155806901364, 0.36908994727156635, 0.40414997030120775, 0.42614038771038076, 0.3120209509688403, 0.4057529954933764, 0.3260221907119308, 0.3618802023427668, 0.4709899545092152, 0.3498383982647257, 0.41415054117898487, 0.40247279325429036, 0.3679523566302214, 0.32004689564239175, 0.3681218228329077, 0.3910261206910418, 0.37583287332496823, 0.4533645837049951, 0.4534329749006663, 0.46480683843308895, 0.38540766075264565, 0.4593958093677675, 0.41216929511578226, 0.0, 0.4747608887804804, 0.3766613947988156, 0.3480609235496084, 0.4342190489036695, 0.4538643446190868, 0.2972178095315592, 0.4665210908834635, 0.0, 0.4100347855773343, 0.4458969632579672, 0.4147075441754524, 0.3956091978744609, 0.4166994632787794, 0.0, 0.4698612114376377, 0.44459479385358314, 0.45998353728005664, 0.34423266627449683, 0.44339829988709273, 0.3208087743858188, 0.33499046749346734, 0.4187560533303449, 0.4707018009928195, 0.46205666317049654, 0.3859572583298333, 0.0, 0.40721832886322684, 0.0, 0.3716875208497137, 0.48349022518597623, 0.44610382752571837, 0.4294598778369194, 0.2456534593977592, 0.4265692348472723, 0.3584633653825982, 0.35139800296072465, 0.0, 0.0, 0.0, 0.4466201862675442, 0.3523603721738321, 0.38640898857956846, 0.3830251015442972, 0.4422903312935297, 0.3961270529493093, 0.42458982204689544, 0.3915465083402976, 0.46258069083770575, 0.3841003406447347, 0.2879992528624649, 0.0, 0.0, 0.44429787333040394, 0.4717074671058845, 0.0, 0.3927803763656502, 0.45957028363606967, 0.37391136254463186, 0.0, 0.4090836023038065, 0.0, 0.4309534038168775, 0.4313076905136646]\n",
        "\n",
        "kld_graph = [0.7300056898789489, 1.1694653021133752, 0.6582040562369684, 1.1142263603662046, 0.8318529166860184, 0.8500070481335991, 0.7234898358384443, 0.9397794415946781, 0.726998247614633, 0.8241942675835762, 0.7265543565505864, 0.8975525931393504, 0.7959301455554484, 0.669376027852106, 0.6923054576777433, 0.8430866798024049, 0.8197445230302883, 1.1839705218869996, 0.6701176466628493, 0.6287766841898392, 0.8159473750157457, 0.756643590754108, 0.6906630843858031, 0.8931309728288316, 0.7144985447747672, 0.8300907000246323, 0.834051631125655, 0.37818748694309756, 0.7661089260302357, 0.5496976276423745, 1.1552599696350314, 1.1139611998440782, 0.9708535963995211, 0.7248635204961643, 0.7987247104279331, 1.0527604136198216, 0.4291175146466038, 0.8817568712791295, 0.977064960959994, 0.8256685296408494, 1.1179781921775627, 1.0158723379775443, 0.6275512853238936, 0.889559830760793, 0.86616539695137, 1.015695888958109, 0.9623901921279748, 0.7535301262975973, 0.6733909419467383, 0.9915513212639595, 0.8950265190593789, 0.7367017095195311, 0.7066241697163015, 0.6304186629758641, 0.6266687545556131, 0.9350300679640836, 0.74445164581268, 0.7132363218902187, 0.899419062924438, 0.9041153125555343, 0.9170253626463758, 0.6777954225459047, 0.6796064196331952, 0.359659861346278, 0.9685228897871683, 0.6872233912545257, 0.9461190163563672, 1.0868593983885828, 0.7128740045675543, 0.7082165618844374, 1.0283505192174982, 0.9770303398102115, 0.9490783233675888, 0.8073872415005704, 0.7649776078191046, 0.7957951660901631, 0.9871173285269387, 0.7410403254178912, 0.6508963809634369, 0.6687174339884687, 0.859265556947042, 0.6807309559224896, 0.9393438592692173, 0.8344844075404826, 0.9832039818569783, 0.6087414672046543, 0.8236330442677694, 0.6797628587297387, 1.235382614666698, 1.1498393769142612, 1.2362334632243672, 0.8164423782212614, 0.8693744754909798, 0.7759678551566488, 0.6634608403603763, 0.41685744869339164, 0.7343632141394312, 0.8113064916022634, 1.0262722025876216, 1.1032334934986978]\n",
        "jsd_graph =[0.40572689452868865, 0.4813096599104728, 0.37274724920541863, 0.45182646849647046, 0.40472723442093994, 0.40754138115633826, 0.38633816629952566, 0.44705624592014304, 0.38367956612670323, 0.4002275883011303, 0.38695315477095393, 0.4187542103541375, 0.40496211598474424, 0.3786508981574111, 0.39717680492744994, 0.40603010429191005, 0.408283973678661, 0.4630105396718603, 0.39274058754513985, 0.3650499782321273, 0.402852165926118, 0.3895585816063566, 0.38845019714391726, 0.4339320594852482, 0.38324987845709724, 0.4039121630728288, 0.420325557946207, 0.2852786702376886, 0.38939581774561316, 0.3369839680655799, 0.46756818119396004, 0.45102940006711667, 0.4566893658812188, 0.386086126785046, 0.39323318162638926, 0.454530816023426, 0.30564345489761596, 0.41733044276279113, 0.45711090061982, 0.4066859195027178, 0.44548471361302966, 0.43001762002444993, 0.3664051800776602, 0.4179066338522689, 0.40804220666223107, 0.46284453146823484, 0.4310261492958148, 0.40493105877435837, 0.3638919214040496, 0.4429928642348839, 0.4276018606833262, 0.39129857365429793, 0.3815681844283498, 0.38290473557663934, 0.36745650365594523, 0.4273650590136626, 0.38773786771474034, 0.38702673306663693, 0.43701637827249645, 0.42669922878383876, 0.4414503565319712, 0.37577487102431545, 0.38371169279552236, 0.28754965702747015, 0.4348691790765962, 0.3754683688074647, 0.44284742084782436, 0.4571780958152055, 0.4051255731165885, 0.3935463398267229, 0.4686754278898076, 0.4499906349221446, 0.43104858078926567, 0.4049067128869286, 0.4101693300689571, 0.40055983664753536, 0.44563792702026545, 0.4115244288142892, 0.3875719847411568, 0.37650062039087717, 0.41643653043166867, 0.3863212113503817, 0.42718551235921753, 0.4167799751288987, 0.44608095400179404, 0.3615201533362695, 0.42967611050141646, 0.3849967713028161, 0.48370776707941016, 0.4832681474441424, 0.46856414784481426, 0.4133377448026442, 0.42057139142854527, 0.3879822334781606, 0.37566998952090147, 0.30391310914144126, 0.3836765924085119, 0.4046502018118133, 0.45583436111455644, 0.4549496473633336]\n",
        "\n",
        "kld_freq = [0.7199990163091441, 1.288577094003375, 0.6876781653361953, 1.0548275305953054, 0.822106467044532, 1.0250455035798383, 0.7642230697261388, 0.8861677097328216, 0.8133151448774971, 1.070300835591361, 0.8902506584557677, 1.0409563178920767, 0.9325518447723371, 0.876780478796326, 0.7527658673801978, 1.109862192707198, 0.9814609731836896, 1.3140823138880307, 0.6336290837851819, 0.5945883813485552, 0.9853710957839535, 0.6425021501485686, 0.7927687220316615, 1.0248072650693685, 0.7909853199013283, 0.917756720574602, 0.7840406497243342, 0.36351808643481, 0.7646289844561274, 0.7901441270581637, 1.3074442360423464, 1.1585353169123405, 0.8390222886295955, 0.7277708334637526, 0.9440759880626365, 1.0119889895584742, 0.4404568982928508, 0.809263712380167, 0.9257424674505709, 0.8088068380486474, 1.1703079980729523, 1.0009809316501646, 0.6645238775763517, 0.9657224564406461, 0.8211600933863579, 0.9475945948239577, 1.0045711656352914, 0.8222920904678139, 0.9173348150335361, 1.0650339501847848, 0.9150650487002507, 0.8592161332260143, 0.7760992513052745, 0.9899705820711331, 0.25402090348903744, 1.0738141312402973, 0.958779532547603, 0.8047759506636604, 0.8837316319163978, 1.0237833505131568, 1.0448370196047883, 0.7598523168375311, 0.7421577154953737, 0.7060657854315938, 0.9310896222377714, 0.7222608882096487, 1.0117940181455518, 1.235252849808724, 0.7749524468673616, 0.6028963762294497, 0.8497471330550408, 1.188247565159837, 1.1064630436808665, 0.8005052123107964, 0.943394967833588, 0.8352263506528074, 1.261395619257123, 0.9057660069747315, 0.7437664404687891, 0.8961443246169718, 0.992520711484797, 0.9310731755674274, 0.8276231800837823, 0.8482506953980145, 0.951658753833869, 0.6529169649183043, 0.6827022343894, 0.8240654539174791, 1.3381892931093735, 0.8883881871861411, 1.3539373370730008, 0.7983410280939688, 0.7374364832394635, 0.841450466192277, 0.7601875735059431, 0.49707873253014384, 0.8610639835107452, 1.0040131802807752, 1.244057337071036, 1.1587067148922152]\n",
        "jsd_freq = [0.39720931225497386, 0.4852511366314636, 0.3872706804927176, 0.46340325681824046, 0.4099997986976341, 0.46005880733558646, 0.4002969489882648, 0.44234415036007135, 0.421925310099122, 0.4693768278740044, 0.4158760771563454, 0.45895034411578084, 0.4402055982830778, 0.41583161778047406, 0.3969658596406603, 0.4911221113095487, 0.445479559429233, 0.5046759063512024, 0.3713131621866397, 0.3478467357335101, 0.4340713440256459, 0.3749889088313121, 0.41151153011543545, 0.4588922511707417, 0.4091819540392327, 0.43419640905267, 0.4065440662077568, 0.2844284268589189, 0.40559652794340256, 0.4014378102932751, 0.4967820570737719, 0.4870344672921085, 0.4078101320044515, 0.4108891690505209, 0.43240798520967044, 0.44935119240437205, 0.30652719506018367, 0.4162684644669471, 0.4354268177375234, 0.39773859743967266, 0.4715521382530961, 0.447962003273332, 0.38610627249095786, 0.4315213182615697, 0.41408626881185323, 0.44327199859245164, 0.44658865113350443, 0.40915999634888267, 0.432579406441209, 0.46516287510699234, 0.43411307155006607, 0.4231658229357147, 0.3998127104906795, 0.45057579185007557, 0.2290297743715948, 0.4760834031030112, 0.4335523857629184, 0.41516752657671646, 0.43874027686937406, 0.4568945731781437, 0.47081245798217464, 0.4056472661746331, 0.3979407366977592, 0.37717446499159896, 0.4340829736581772, 0.3881235315133434, 0.44593482069341067, 0.4900066637617449, 0.4013769642616592, 0.3620793700689522, 0.42967583582364666, 0.5092855744997147, 0.4815715537389554, 0.4107153861102546, 0.4324135002174301, 0.41260020432057676, 0.47584168236171737, 0.45241197157420976, 0.39985777827917024, 0.44433796189167596, 0.4507521245052968, 0.43070964328348293, 0.4142477213322459, 0.4282527282335757, 0.4372838180953955, 0.37491735876279997, 0.3816171949760131, 0.42752970547470326, 0.4951776614304662, 0.43596184440374197, 0.5136152666821995, 0.41795562355788785, 0.4099793980911211, 0.4202847642635409, 0.4098869056748805, 0.32936378459450627, 0.4188613183589922, 0.4511300428259509, 0.48116143642937387, 0.4832579831809677]\n",
        "\n",
        "kld_bert = [0.5304973289536807, 0.9637565778806534, 0.6480372126828674, 1.0598080231113771, 0.6596100015838781, 0.961695906703837, 0.7166647888742338, 0.7186481524160726, 0.7381156941943346, 0.862578381459093, 0.640964951575515, 0.9311825467942473, 0.9272953183638052, 0.7276216750871183, 0.6588225045697049, 0.6566555543439125, 0.8280155155966138, 1.0949900884823685, 0.5425052597443711, 0.5551566026817091, 0.6241933076999967, 0.7095698631706058, 0.673119474083796, 0.7905761619263152, 0.6242677652059733, 0.9976209278947502, 0.8732555087957017, 0.3591183501963573, 0.849222025683259, 0.6984104719996489, 1.1119208861668393, 1.3029989737257086, 0.848117816655112, 0.7022579061530491, 0.8819495142169931, 1.0727132887692705, 0.4128020284386661, 1.0196308022374296, 0.8100468503356244, 0.8140667038037898, 1.0655508009892813, 0.9815567821115196, 0.6109622208530741, 0.9214327515858699, 0.8621839946107106, 0.8865849235084697, 0.9109970051804893, 0.6460361018165194, 0.6222885810373494, 1.1783418123357225, 0.9630834604445799, 0.8346854932805244, 0.7593274330843409, 0.6464519863167456, 0.49828975266184294, 0.9051504603154208, 0.732859508533769, 0.5883009844450979, 0.7792483712743699, 0.9471174310788678, 1.0609638247850413, 0.6757815021318018, 0.7330258748881644, 0.5559081557870065, 0.816289124740535, 0.7902729881207241, 1.1177058787774334, 1.239642792326871, 0.6555347046968876, 0.658702773961174, 0.7137874341646481, 0.8637931750291281, 0.848320641984561, 0.6719231236172478, 0.9253554812401936, 0.8316313366157146, 0.8022974954429185, 0.7952313732601919, 0.6158349071814838, 0.5618559325841107, 0.8884490172145723, 0.7791281893423676, 0.7782833262523281, 0.6905315345050577, 0.9500806809207483, 0.520986391642601, 0.7570821079351988, 0.7735853735792704, 1.2298921125572484, 0.9594217032022658, 1.3641683061680994, 0.7393966469792548, 0.8470968560346392, 0.7198647080673972, 0.7549458042745456, 0.3977209748034853, 0.6943210948819042, 1.161706335085129, 0.8618877660548809, 1.1321097725975433]\n",
        "jsd_bert = [0.3415987406274615, 0.44210817953984255, 0.36754072352737316, 0.45747025361210253, 0.3776636969160695, 0.43613048111817065, 0.38769332712879784, 0.40006278618025437, 0.38396390400454056, 0.41504584364896485, 0.36411206076899383, 0.44146210491708476, 0.43135545627620764, 0.39305819499632433, 0.3792981413399124, 0.37102768524332247, 0.4199600907573846, 0.4696523409213456, 0.3497164250851513, 0.34769639864097374, 0.3650174249512146, 0.3963937140812465, 0.378200864675577, 0.4063622141564362, 0.37793875527006626, 0.4443886894314313, 0.4191759712034987, 0.293562313542009, 0.4148977973519625, 0.39130608821245505, 0.4694147665112849, 0.4852143144775648, 0.41804461405469906, 0.3928175122092915, 0.4248196700538005, 0.4538732997382873, 0.30002130235463176, 0.44292963396396, 0.41513915475863167, 0.4088110560382302, 0.4563105561278012, 0.44344671654339, 0.36365103369746005, 0.43066126597339593, 0.4179190556897388, 0.4327550268949804, 0.42079462500194903, 0.3730467208716036, 0.36888862245822085, 0.4711803975571647, 0.44315642464002536, 0.41074213859580616, 0.4014344261972164, 0.3826689095336573, 0.3437649159827409, 0.4311520754593707, 0.3910529665278217, 0.3617512551361415, 0.4018874945951712, 0.433170300731974, 0.450713242242193, 0.38473922667292304, 0.3792486433434342, 0.3524824067130604, 0.4124837311785787, 0.40966758479396037, 0.4826046735492886, 0.4948677025382312, 0.37409961814153053, 0.38192050956733753, 0.38439736853691475, 0.4130382885981921, 0.4125240332798613, 0.3834279654839512, 0.4358320838959415, 0.4122249822494063, 0.4183927825287325, 0.4143664524146691, 0.36885657314095305, 0.3560934992577938, 0.4255780726272146, 0.40303959960950386, 0.3929390018754541, 0.3850112199672151, 0.4296328278415279, 0.345742785494755, 0.4022775830894425, 0.39742295362622704, 0.4769423924037395, 0.4447968521672965, 0.5062728465534658, 0.3964832887988105, 0.4189252176062037, 0.39492882178987415, 0.3995083695658513, 0.3053574689348451, 0.3827991427645105, 0.47660273996159774, 0.4211118754911175, 0.4674714604609634]"
      ],
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 536
        },
        "id": "3YDV4hgB-Poh",
        "outputId": "43d31962-947d-46f9-83f5-28d4faa1a373"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "plt.figure(figsize=(10,8))\n",
        "\n",
        "# plt.subplot(121)\n",
        "plt.style.use('ggplot')\n",
        "plt.plot(kld_freq,'r',label= \"Frequency-Based Approach\")\n",
        "plt.plot(kld_graph,'b',label=\"Graph-Based Approach\")\n",
        "plt.plot(kld_extr,'g',label=\"Centroid based K-means Approach\")\n",
        "plt.plot(kld_bert,'y',label=\"BERT-Based Approach\")\n",
        "plt.xlabel(\"$Test\\ Dataset$\")\n",
        "plt.ylabel(\"$Closeness\\ to\\ Original\\ Document$\")\n",
        "plt.title(\"$KL\\  Divergance$\")\n",
        "plt.legend()\n"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f64d9a118d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "X55b76dGXDBf",
        "outputId": "fee5c291-8fe1-4b62-e09f-830fadec0dbe"
      },
      "source": [
        "plt.figure(figsize=(10,8))\n",
        "plt.style.use('ggplot')\n",
        "plt.plot(jsd_freq,'r',label= \"Frequency-Based Approach\")\n",
        "plt.plot(jsd_graph,'b',label=\"Graph-Based Approach\")\n",
        "plt.plot(jsd_extr,'g',label=\"Centroid based K-means Approach\")\n",
        "plt.plot(jsd_bert,'y',label=\"BERT-Based Approach\")\n",
        "plt.xlabel(\"$Test\\ Dataset$\")\n",
        "plt.ylabel(\"$Closeness\\ to\\ Original\\ Document$\")\n",
        "plt.title(\"$JS\\  Divergance$\")\n",
        "plt.legend()\n",
        "plt.savefig(\"evaluation.jpg\")\n",
        "plt.show()"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SQV8rgTTYhC0"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}