README.md

Sequence-to-Sequence with Attention Model for Text Summarization.

Authors:

Xin Pan (xpan@google.com, github:panyx0718), Peter Liu (peterjliu@google.com, github:peterjliu)

Introduction

The core model is the traditional sequence-to-sequence model with attention. It is customized (mostly inputs/outputs) for the text summarization task. The model has been trained on Gigaword dataset and achieved state-of-the-art results (as of June 2016).

The results described below are based on model trained on multi-gpu and multi-machine settings. It has been simplified to run on only one machine for open source purpose.

Dataset

We used the Gigaword dataset described in Rush et al. A Neural Attention Model for Sentence Summarization.

We cannot provide the dataset due to the license. See ExampleGen in data.py about the data format. data/data contains a toy example. Also see data/vocab for example vocabulary format. In How To Run below, users can use toy data and vocab provided in the data/ directory to run the training by replacing the data directory flag.

data_convert_example.py contains example of convert between binary and text.

Experiment Result

8000 examples from testset are sampled to generate summaries and rouge score is calculated for the generated summaries. Here is the best rouge score on Gigaword dataset:

ROUGE-1 Average_R: 0.38272 (95%-conf.int. 0.37774 - 0.38755)

ROUGE-1 Average_P: 0.50154 (95%-conf.int. 0.49509 - 0.50780)

ROUGE-1 Average_F: 0.42568 (95%-conf.int. 0.42016 - 0.43099)

ROUGE-2 Average_R: 0.20576 (95%-conf.int. 0.20060 - 0.21112)

ROUGE-2 Average_P: 0.27565 (95%-conf.int. 0.26851 - 0.28257)

ROUGE-2 Average_F: 0.23126 (95%-conf.int. 0.22539 - 0.23708)

Configuration:

Following is the configuration for the best trained model on Gigaword:

batch_size: 64

bidirectional encoding layer: 4

article length: first 2 sentences, total words within 120.

summary length: total words within 30.

word embedding size: 128

LSTM hidden units: 256

Sampled softmax: 4096

vocabulary size: Most frequent 200k words from dataset's article and summaries.

How To Run

Prerequisite: install TensorFlow and Bazel.

# cd to your workspace
# 1. Clone the textsum code to your workspace 'textsum' directory.
# 2. Create an empty 'WORKSPACE' file in your workspace.
# 3. Move the train/eval/test data to your workspace 'data' directory.
#    In the following example, I named the data training-*, test-*, etc.
#    If your data files have different names, update the --data_path.
#    If you don't have data but want to try out the model, copy the toy
#    data from the textsum/data/data to the data/ directory in the workspace.
$ ls -R
.:
data  textsum  WORKSPACE

./data:
vocab  test-0  training-0  training-1  validation-0 ...(omitted)

./textsum:
batch_reader.py       beam_search.py       BUILD    README.md                    seq2seq_attention_model.py  data
data.py  seq2seq_attention_decode.py  seq2seq_attention.py        seq2seq_lib.py

./textsum/data:
data  vocab

$ bazel build -c opt --config=cuda textsum/...

# Run the training.
$ bazel-bin/textsum/seq2seq_attention \
    --mode=train \
    --article_key=article \
    --abstract_key=abstract \
    --data_path=data/training-* \
    --vocab_path=data/vocab \
    --log_root=textsum/log_root \
    --train_dir=textsum/log_root/train

# Run the eval. Try to avoid running on the same machine as training.
$ bazel-bin/textsum/seq2seq_attention \
    --mode=eval \
    --article_key=article \
    --abstract_key=abstract \
    --data_path=data/validation-* \
    --vocab_path=data/vocab \
    --log_root=textsum/log_root \
    --eval_dir=textsum/log_root/eval

# Run the decode. Run it when the model is mostly converged.
$ bazel-bin/textsum/seq2seq_attention \
    --mode=decode \
    --article_key=article \
    --abstract_key=abstract \
    --data_path=data/test-* \
    --vocab_path=data/vocab \
    --log_root=textsum/log_root \
    --decode_dir=textsum/log_root/decode \
    --beam_size=8

Examples:

The following are some text summarization examples, including experiments using dataset other than Gigaword.

article: novell inc. chief executive officer eric schmidt has been named chairman of the internet search-engine company google .

human: novell ceo named google chairman

machine: novell chief executive named to head internet company

======================================

article: gulf newspapers voiced skepticism thursday over whether newly re - elected us president bill clinton could help revive the troubled middle east peace process but saw a glimmer of hope .

human: gulf skeptical about whether clinton will revive peace process

machine: gulf press skeptical over clinton 's prospects for peace process

======================================

article: the european court of justice ( ecj ) recently ruled in lock v british gas trading ltd that eu law requires a worker 's statutory holiday pay to take commission payments into account - it should not be based solely on basic salary . the case is not over yet , but its outcome could potentially be costly for employers with workers who are entitled to commission . mr lock , an energy salesman for british gas , was paid a basic salary and sales commission on a monthly basis . his sales commission made up around 60 % of his remuneration package . when he took two weeks ' annual leave in december 2012 , he was paid his basic salary and also received commission from previous sales that fell due during that period . lock obviously did not generate new sales while he was on holiday , which meant that in the following period he suffered a reduced income through lack of commission . he brought an employment tribunal claim asserting that this amounted to a breach of the working time regulations 1998 .....deleted rest for readability...

abstract: will british gas ecj ruling fuel holiday pay hike ?

decode: eu law requires worker 's statutory holiday pay

======================================

article: the junior all whites have been eliminated from the fifa u - 20 world cup in colombia with results on the final day of pool play confirming their exit . sitting on two points , new zealand needed results in one of the final two groups to go their way to join the last 16 as one of the four best third place teams . but while spain helped the kiwis ' cause with a 5 - 1 thrashing of australia , a 3 - 0 win for ecuador over costa rica saw the south americans climb to second in group c with costa rica 's three points also good enough to progress in third place . that left the junior all whites hopes hanging on the group d encounter between croatia and honduras finishing in a draw . a stalemate - and a place in the knockout stages for new zealand - appeared on the cards until midfielder marvin ceballos netted an 81st minute winner that sent guatemala through to the second round and left the junior all whites packing their bags . new zealand finishes the 24 - nation tournament in 17th place , having claimed their first ever points at this level in just their second appearance at the finals .

abstract: junior all whites exit world cup

decoded: junior all whites eliminated from u- 20 world cup