Deep Communicating Agents for Abstractive Summarization

Asli Celikyilmaz, Antoine Bosselut, Xiaodong He, Yejin Choi


Abstract
We present deep communicating agents in an encoder-decoder architecture to address the challenges of representing a long document for abstractive summarization. With deep communicating agents, the task of encoding a long text is divided across multiple collaborating agents, each in charge of a subsection of the input text. These encoders are connected to a single decoder, trained end-to-end using reinforcement learning to generate a focused and coherent summary. Empirical results demonstrate that multiple communicating encoders lead to a higher quality summary compared to several strong baselines, including those based on a single encoder or multiple non-communicating encoders.
Anthology ID:
N18-1150
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Editors:
Marilyn Walker, Heng Ji, Amanda Stent
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1662–1675
Language:
URL:
https://aclanthology.org/N18-1150
DOI:
10.18653/v1/N18-1150
Bibkey:
Cite (ACL):
Asli Celikyilmaz, Antoine Bosselut, Xiaodong He, and Yejin Choi. 2018. Deep Communicating Agents for Abstractive Summarization. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1662–1675, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Deep Communicating Agents for Abstractive Summarization (Celikyilmaz et al., NAACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/N18-1150.pdf
Data
CNN/Daily MailNew York Times Annotated Corpus