Skip to content
master
Go to file
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
log
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

README.md

BanditSum

This repository contains the pre-processed data and code for our EMNLP 2018 paper "BanditSum: Extractive Summarization as a Contextual Bandit". Please contact me at yue.dong2@mail.mcgill.ca for any question.

Please cite this paper if you use our code or data.

@inproceedings{dong2018banditsum,
  title={BanditSum: Extractive Summarization as a Contextual Bandit},
  author={Dong, Yue and Shen, Yikang and Crawford, Eric and van Hoof, Herke and Cheung, Jackie Chi Kit},
  booktitle={Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
  pages={3739--3748},
  year={2018}
}

New Updates:

It was recently discovered that our model can achieve better performance than the one reported in the paper (trained with two epochs) if trained to four epochs on CNN/DailyMail:

BanditSum reported in the paper: ROUGE-1 41.5 ROUGE-2 18.7 ROUGE-L 37.6

BanditSum trained after 4 epochs: ROUGE-1 41.68 ROUGE-2 18.78 ROUGE-L 38.00

CNN/DailyMail Dataset

Instructions to download our preprocessed CNN/DailyMail Dataset can be found here. https://github.com/JafferWilson/Process-Data-of-CNN-DailyMail

Our Test Output:

https://drive.google.com/file/d/1tMiWuRzvDfHGwDILDXT2WFpyFcuHSK1n/view?usp=sharing

Our Pre-trained Model:

Test data: https://drive.google.com/file/d/1PCl0VVfhlcEaz-eSc5alP_U8uaVQGc_P/view?usp=sharing

Pre-trained model: https://drive.google.com/file/d/13UB2GH_TT5SPQaYydnxYXYHClD4pbOIn/view?usp=sharing

The vocab file: https://drive.google.com/file/d/1W0QQkz5VNCk-YAnpSRc0ONFgR5SPGDA8/view?usp=sharing

Installation

Our code is written with python 2.7. Please see the modification from David Beauchemin https://github.com/davebulaval/BanditSum if you intend to convert the code to python 3.7.

Our code requires PyTorch version >= 0.4.0. Please follow the instructions here: https://github.com/pytorch/pytorch#installation.

After PyTorch is installed, you can run our model through main.py.

About

This repository contains data and code for our EMNLP 2018 paper "BanditSum: Extractive Summarization as a Contextual Bandit.

Resources

Releases

No releases published

Packages

No packages published

Languages