Note: this code is no longer actively maintained.
- attention based summarization on tensorflow using seq2seq model
- my graduation project code
- do not provide data for the time
- ubuntu 16.04 lts
- anaconda python 3.6
- recompiled tensorflow r1.7 gpu version
- CUDA 9.0
- cudnn 7.1.2
- rouge
- 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 Word2Vec,then runpython main.py -train
to train the model andpython 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.
- finish word embedding matrix
- build seq2seq model
- test lstm and gru core
- test bidirectional core
- fix infer problem
- test multilayer with dropout core
- fix lazy loading
- fix pre-processing
- try training with non-mentor model
- secondary activation
- test attention decoder(luong attention)
- choose last batch in each epoch as the validation set
- learning rate decay:gradient descent,low init value,decay=0.995
- cut vocab size to 3000,replace unusual word to unk
- enlarge rnn hidden units size
- fix word embedding matrix and try to load model
- divide infer and train into two graphs
- use rouge to value model
- save each test result
-
fix unk problems - train sentiment classification svm
- add sentiment-blended word embeddings
- test sentiment classify
- use larger corpus
- collect ROUGE
- ROUGE files collected in the './ROUGE_ANSWER'