openNMT.0.md

Motivations

• Replicate results for Text Summarization task on Gigaword (see 'About')
• Getting started with Text Summarization using OpenNMT (src)
• Getting started with ROUGE scoring using files2rouge (src)

About

• Reference: http://opennmt.net//Models/#english-summarization
• Dataset: https://github.com/harvardnlp/sent-summary
• Expected results:
  • R1: 33.13
  • R2: 16.09
  • RL: 31.00
• OpenNMT v0.2.0. (precisely using commit from the 4th of Jan., 2017, 561994adcd147f9f77cc744a041152c3182a9300)
• file2rouge commit: 5397befa8397017964d21aa61a4e399dedd5c340

Setup

git clone https://github.com/OpenNMT/OpenNMT.git opennmt
git clone --recursive https://github.com/pltrdy/files2rouge.git files2rouge

Download data from here and extract (tar -xzf summary.tar.gz) to ./data.

We assume that your file system is like:

./   
  opennmt/   
  data/   
  file2rouge/   

Building model

Following the guide

# First, move to OpenNMT dir
cd opennmt

1) Preprocess

th preprocess.lua -train_src ../data/train/train.article.txt -train_tgt ../data/train/train.title.txt -valid_src ../data/train/valid.article.filter.txt -valid_tgt ../data/train/valid.title.filter.txt -save_data ../data/train/textsum

2) Train

th train.lua -data ./textsum_train/textsum-train.t7  -save_model textsum

or using GPU:

th train.lua -data ./textsum_train/textsum_model-train.t7  -save_model textsum -gpuid 1

3) Generate summary

th translate.lua -model textsum_final.t7 -src ../data/Giga/inputs.txt

(add -gpuid 1 if you trained the model using GPU)
The output will be in pred.txt

ROUGE Scoring using files2rouge

cd ../files2rouge
./files2rouge --ref ../data/Giga/task1_ref0.txt --summ ../opennmt/pred.txt

Results

ROUGE-1	ROUGE-2	ROUGE-L
34.2	16.2	31.9