Our code is modified based on transformer-pointer-generator. The original project only supports datasets in Chinese format, and can not directly process Datasets in English format.

A Abstractive Summarization Implementation with Transformer and Pointer-generator

when I wanted to get summary by neural network, I tried many ways to generate abstract summary, but the result was not good. when I heared 2018 byte cup, I found some information about it, and the champion's solution attracted me, but I found some websites, like github gitlab, I didn't find the official code, so I decided to implement it.

Model Structure

Based

My model is based on Attention Is All You Need and Get To The Point: Summarization with Pointer-Generator Networks

Change

• The pointer-generator model has two mechanisms, which are copy mechanism and coverage mechanism, I found some materials, they show the Coverage mechanism doesn't suit short summary, so I didn't use this mechanism, just use the first one.
• Pointer generator model has a inadequacy, which can let the loss got nan, I tried some times and wanted to fix it, but the result was I can't, I think the reason was when calculate final logists, it will extend vocab length to oov and vocab length, it will get more zeroes. so I delete the mechanism of extend final logists, just use their mechanism of deocode from article and vocab. there is more detail about it, in this model, I just use word than vocab, this idea is from bert.

Structure

Requirements

• python==3.x (Let's move on to python 3 if you still use python 2)
• tensorflow==1.12.0
• tqdm>=4.28.1
• jieba>=0.3x
• sumeval>=0.2.0

Preprocessing

• STEP 1. Create folder dataset/your_dataset_name and create train_source.txt, train_target.txt, eval_source.txt, eval_target.txt, test_source.txt, test_target.txt. Noted that each file contains the corresponding text line by line.
• STEP 2. Run command python merge_source_target.py to get the processed files(train.csv, eval.csv, test.csv and vocab)

Training

Run the following command.

python train.py

Check hparams.py to see which parameters are possible. For example,

python train.py --logdir log/conala --evaldir eval/conala --train dataset/conala/train.csv --eval dataset/conala/eval.csv --vocab dataset/conala/vocab --vocab_size 4137 --maxlen1 200 --maxlen2 50 --batch_size 32

My code also improve multi gpu to train this model, if you have more than one gpu, just run like this

python train.py --logdir log/conala --evaldir eval/conala --train dataset/conala/train.csv --eval dataset/conala/eval.csv --vocab dataset/conala/vocab --vocab_size 4137 --maxlen1 200 --maxlen2 50 --batch_size 32 --gpu_nums=1

name	type	detail
vocab_size	int	vocab size
train	str	train dataset dir
eval	str	eval dataset dir
test	str	data for calculate rouge score
vocab	str	vocabulary file path
batch_size	int	train batch size
eval_batch_size	int	eval batch size
lr	float	learning rate
warmup_steps	int	warmup steps by learing rate
logdir	str	log directory
num_epochs	int	the number of train epoch
evaldir	str	evaluation dir
d_model	int	hidden dimension of encoder/decoder
d_ff	int	hidden dimension of feedforward layer
num_blocks	int	number of encoder/decoder blocks
num_heads	int	number of attention heads
maxlen1	int	maximum length of a source sequence
maxlen2	int	maximum length of a target sequence
dropout_rate	float	dropout rate
beam_size	int	beam size for decode
gpu_nums	int	gpu amount, which can allow how many gpu to train this model, default 1

Note

Don't change the hyper-parameters of transformer util you have good solution, it will let the loss can't go down! if you have good solution, I hope you can tell me.

Evaluation

Run the following command:

python pred.py --ckpt log/conala/trans_pointerE3468L0.37-3468 --test dataset/conala/test.csv --vocab dataset/conala/vocab --vocab_size 4137 --maxlen1 200 --maxlen2 50

Run command python merge_source_target.py to get the BLEU-4, ROUGE-L and METEOR scores.

If you like it, and think it useful for you, hope you can star.