https://github.com/thinkwee/abstract_summarization_rnn

RNN Seq2Seq Based Abstract Summarization(ABS) On Tensorflow
https://github.com/thinkwee/abstract_summarization_rnn

lstm nlp python rnn seq2seq summarization tensorflow-seq2seq tensroflow

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RNN Seq2Seq Based Abstract Summarization(ABS) On Tensorflow

• Host: GitHub
• URL: https://github.com/thinkwee/abstract_summarization_rnn
• Owner: thinkwee
• Created: 2018-01-21T14:25:58.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2019-03-16T09:19:44.000Z (about 7 years ago)
• Last Synced: 2025-01-12T21:21:49.221Z (over 1 year ago)
• Topics: lstm, nlp, python, rnn, seq2seq, summarization, tensorflow-seq2seq, tensroflow
• Language: Python
• Homepage:
• Size: 198 MB
• Stars: 10
• Watchers: 3
• Forks: 4
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
*Note: this code is no longer actively maintained.*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](https://drive.google.com/open?id=0B0ChLbwT19XcOVZFdm5wNXA5ODg)

- 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 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

- [x] finish word embedding matrix

- [x] build seq2seq model

- [x] test lstm and gru core

- [x] test bidirectional core

- [x] fix infer problem

- [x] test multilayer with dropout core

- [x] fix lazy loading

- [x] fix pre-processing

- [x] try training with non-mentor model

- [x] secondary activation

- [x] test attention decoder(luong attention)

- [x] choose last batch in each epoch as the validation set

- [x] learning rate decay:gradient descent,low init value,decay=0.995

- [x] cut vocab size to 3000,replace unusual word to unk

- [x] enlarge rnn hidden units size

- [x] fix word embedding matrix and try to load model

- [x] divide infer and train into two graphs

- [x] use rouge to value model

- [x] save each test result

- [ ] ~~fix unk problems~~

- [x] train sentiment classification svm

- [x] add sentiment-blended word embeddings

- [x] test sentiment classify

- [X] use larger corpus

- [x] collect ROUGE

# current effect

- ROUGE files collected in the './ROUGE_ANSWER'