BanditSum: Extractive Summarization as a Contextual Bandit

Yue Dong, Yikang Shen, Eric Crawford, Herke van Hoof, Jackie Chi Kit Cheung


Abstract
In this work, we propose a novel method for training neural networks to perform single-document extractive summarization without heuristically-generated extractive labels. We call our approach BanditSum as it treats extractive summarization as a contextual bandit (CB) problem, where the model receives a document to summarize (the context), and chooses a sequence of sentences to include in the summary (the action). A policy gradient reinforcement learning algorithm is used to train the model to select sequences of sentences that maximize ROUGE score. We perform a series of experiments demonstrating that BanditSum is able to achieve ROUGE scores that are better than or comparable to the state-of-the-art for extractive summarization, and converges using significantly fewer update steps than competing approaches. In addition, we show empirically that BanditSum performs significantly better than competing approaches when good summary sentences appear late in the source document.
Anthology ID:
D18-1409
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3739–3748
Language:
URL:
https://aclanthology.org/D18-1409
DOI:
10.18653/v1/D18-1409
Bibkey:
Cite (ACL):
Yue Dong, Yikang Shen, Eric Crawford, Herke van Hoof, and Jackie Chi Kit Cheung. 2018. BanditSum: Extractive Summarization as a Contextual Bandit. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3739–3748, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
BanditSum: Extractive Summarization as a Contextual Bandit (Dong et al., EMNLP 2018)
Copy Citation:
PDF:
https://aclanthology.org/D18-1409.pdf
Attachment:
 D18-1409.Attachment.pdf
Video:
 https://vimeo.com/306160623
Code
 yuedongP/BanditSum
Data
CNN/Daily Mail