heavenask / transformer-pointer-generator

A Abstractive Summarization Implementation with Transformer and Pointer-generator

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.

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

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

Training

• STEP 1. download the dataset, pwd is ayn6, the dataset is LCSTS by pre processed, so you will see very different dataset structure with LCSTS in the file each line is abstract and article, they split by ",", if you worry the amount of the dataset is different between my and LCSTS, don't worry, the amout of the dataset is same as LCSTS.
• STEP 2. Run the following command.

python train.py

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

python train.py --logdir myLog --batch_size 32 --train myTrain --eval myEval

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 myLog --batch_size 32 --train myTrain --eval myEval --gpu_nums=myGPUNums

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

Loss

• Transformer-Pointer generator

* Transformer

As you see, transformer-pointer generator model can let the loss go down very quickly!

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