tensorflow-text-summarization

Simple Tensorflow implementation of text summarization using seq2seq library.

Model

Encoder-Decoder model with attention mechanism.

Word Embedding

Used Glove pre-trained vectors to initialize word embedding.

Encoder

Used LSTM cell with stack_bidirectional_dynamic_rnn.

Decoder

Used LSTM BasicDecoder for training, and BeamSearchDecoder for inference.

Attention Mechanism

Used BahdanauAttention with weight normalization.

Requirements

Python 3
Tensorflow (>=1.8.0)
pip install -r requirements.txt

Usage

Prepare data

Dataset is available at harvardnlp/sent-summary. Locate the summary.tar.gz file in project root directory. Then,

$ python prep_data.py

To use Glove pre-trained embedding, download it via

$ python prep_data.py --glove

Train

We used sumdata/train/train.article.txt and sumdata/train/train.title.txt for training data. To train the model, use

$ python train.py

To use Glove pre-trained vectors as initial embedding, use

$ python train.py --glove

Additional Hyperparamters

$ python train.py -h
usage: train.py [-h] [--num_hidden NUM_HIDDEN] [--num_layers NUM_LAYERS]
                [--beam_width BEAM_WIDTH] [--glove]
                [--embedding_size EMBEDDING_SIZE]
                [--learning_rate LEARNING_RATE] [--batch_size BATCH_SIZE]
                [--num_epochs NUM_EPOCHS] [--keep_prob KEEP_PROB] [--toy]
optional arguments:
  -h, --help            show this help message and exit
  --num_hidden NUM_HIDDEN
                        Network size.
  --num_layers NUM_LAYERS
                        Network depth.
  --beam_width BEAM_WIDTH
                        Beam width for beam search decoder.
  --glove               Use glove as initial word embedding.
  --embedding_size EMBEDDING_SIZE
                        Word embedding size.
  --learning_rate LEARNING_RATE
                        Learning rate.
  --batch_size BATCH_SIZE
                        Batch size.
  --num_epochs NUM_EPOCHS
                        Number of epochs.
  --keep_prob KEEP_PROB
                        Dropout keep prob.
  --toy                 Use only 5K samples of data

Test

Generate summary of each article in sumdata/train/valid.article.filter.txt by

$ python test.py

It will generate result summary file result.txt. Check out ROUGE metrics between result.txt and sumdata/train/valid.title.filter.txt using pltrdy/files2rouge.

Sample Summary Output

"general motors corp. said wednesday its us sales fell ##.# percent in december and four percent in #### with the biggest losses coming from passenger car sales ."
> Model output: gm us sales down # percent in december
> Actual title: gm december sales fall # percent
"japanese share prices rose #.## percent thursday to <unk> highest closing high for more than five years as fresh gains on wall street fanned upbeat investor sentiment , dealers said ."
> Model output:  tokyo shares close # percent higher
> Actual title: tokyo shares close up # percent
"hong kong share prices opened #.## percent higher thursday on follow-through interest in properties after wednesday 's sharp gains on abating interest rate worries , dealers said ."
> Model output: hong kong shares open higher
> Actual title: hong kong shares open higher as rate worries ease
"the dollar regained some lost ground in asian trade thursday in what was seen as a largely technical rebound after weakness prompted by expectations of a shift in us interest rate policy , dealers said ."
> Model output: dollar stable in asian trade
> Actual title: dollar regains ground in asian trade
"the final results of iraq 's december general elections are due within the next four days , a member of the iraqi electoral commission said on thursday ."
> Model output: iraqi election results due in next four days
> Actual title: iraqi election final results out within four days
"microsoft chairman bill gates late wednesday unveiled his vision of the digital lifestyle , outlining the latest version of his windows operating system to be launched later this year ."
> Model output: bill gates unveils new technology vision
> Actual title: gates unveils microsoft 's vision of digital lifestyle

Pre-trained Model

To test with pre-trained model, download pre_trained.zip, and locate it in the project root directory. Then,

$ unzip pre_trained.zip
$ python test.py