Note: this code is no longer actively maintained.
introduction
- attention based summarization on tensorflow using seq2seq model
- my graduation project code
- do not provide data for the time
environment
- ubuntu 16.04 lts
- anaconda python 3.6
- recompiled tensorflow r1.7 gpu version
- CUDA 9.0
- cudnn 7.1.2
- rouge
run
- This work use Gigaword dataset which is not for public. You need fetch the data yourself.
- The SentiWordNet 3.0 dataset can be found here :SentiWordNet3.0
- The codes are written in an early version of tensorflow. I do not recommend run this code directly. Just for reference.
- run
python main.py -help
for help.
- run
python main.py -w2v
to train the wordvector from Gigaword dataset using Word2Vecthen run python main.py -train
to train the model and python main.py -test
to test the model(just get the output of testset).
- you need install ROUGE to test the output. All the results are collected in the original PERL version of ROUGE. Using PyRouge make cause the result a little bit higher.
progress
current effect
- ROUGE files collected in the './ROUGE_ANSWER'