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 [email protected] 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}
}
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 Pull request from David Beauchemin 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.