Extractive Summarizer

Topic based extractive summarizer built to summarize English texts.

Sitemap

• summarize.py: Contains code for summarization, including LdaSummarizer class
• Time_and_Sentence_Length_Analysis.ipynb: Contains analysis of time it takes to summarize and sentence length penalty (penalizing longer sentences, making them less likely to be inlcuded in the summary).
• un_debates_summarize.py: Script to run summarization on first 100 UN General Assembly Statements, saves summaries in summary column and writes to csv file. Easily modifiable for any subset of the data/all the data.

Approach to the problem:

Summaries describe the main ideas in a text, and I approached the problem with the objective of finding sentences that best cover the main topics in a text. With that in mind, for each piece of text, topic analysis (latent dirchlet allocation) was used to determine what the main topics were. The most discussed topics (based on the number of sentences a topic came up in) were then selected, and the sentence with the highest probability for each of these topics found. These sentence were extracted, ordered based on their when they came up in the orginal text, and put together as a summary. Going forward, it would be interesting to experiment with using this same basic concept, building a summary with main topics, in an abstractive fashion using neural networks, specifically transformers with attention mechanisms, which have proved very effective for nlp tasks. See this paper as an example.

Measuring performance:

Below are possible ways to evaluate performance in this problem.

• Qualitative approach: an individual comparing output to actual text on a small subset of the data
• Resource intensive: if resources are available have people create ground truth summaries and combine the BLEU and ROUGE metrics to create an F1 metric representative of the lexicon overlap between automatically generated summaries and the ground truth created by people
• Transfer approach: evalute the summarization method on a different dataset of texts that already have ground truth summaries using the BLEU and ROUGE metrics. Use this evaluation as a proxy for performance on the UN General Assembly Statements summary problem.

Citations

• Comparative Summarization via Latent Comparative Summarization via Latent Dirichlet Allocation Dirichlet Allocation
• Multi-Document Summarization Using K-Means and Latent Dirichlet Allocation (LDA) – Significance Sentences
• Approaches to Text Summarization: An Overview