A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents

Arman Cohan, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Seokhwan Kim, Walter Chang, Nazli Goharian

Abstract
Neural abstractive summarization models have led to promising results in summarizing relatively short documents. We propose the first model for abstractive summarization of single, longer-form documents (e.g., research papers). Our approach consists of a new hierarchical encoder that models the discourse structure of a document, and an attentive discourse-aware decoder to generate the summary. Empirical results on two large-scale datasets of scientific papers show that our model significantly outperforms state-of-the-art models.
Anthology ID:
N18-2097
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short 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:
615–621
Language:
URL:
https://aclanthology.org/N18-2097
DOI:
10.18653/v1/N18-2097
Bibkey:
Cite (ACL):
Arman Cohan, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Seokhwan Kim, Walter Chang, and Nazli Goharian. 2018. A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 615–621, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents (Cohan et al., NAACL 2018)
Copy Citation:
PDF:
https://preview.aclanthology.org/fix-dup-bibkey/N18-2097.pdf
Code
 acohan/long-summarization +  additional community code
Data
arXiv Summarization Dataset, Arxiv HEP-TH citation graph, CNN/Daily Mail, Pubmed
PDF Search Code