Neural Extractive Summarization with Hierarchical Attentive Heterogeneous Graph Network

Ruipeng Jia, Yanan Cao, Hengzhu Tang, Fang Fang, Cong Cao, Shi Wang


Abstract
Sentence-level extractive text summarization is substantially a node classification task of network mining, adhering to the informative components and concise representations. There are lots of redundant phrases between extracted sentences, but it is difficult to model them exactly by the general supervised methods. Previous sentence encoders, especially BERT, specialize in modeling the relationship between source sentences. While, they have no ability to consider the overlaps of the target selected summary, and there are inherent dependencies among target labels of sentences. In this paper, we propose HAHSum (as shorthand for Hierarchical Attentive Heterogeneous Graph for Text Summarization), which well models different levels of information, including words and sentences, and spotlights redundancy dependencies between sentences. Our approach iteratively refines the sentence representations with redundancy-aware graph and delivers the label dependencies by message passing. Experiments on large scale benchmark corpus (CNN/DM, NYT, and NEWSROOM) demonstrate that HAHSum yields ground-breaking performance and outperforms previous extractive summarizers.
Anthology ID:
2020.emnlp-main.295
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3622–3631
Language:
URL:
https://aclanthology.org/2020.emnlp-main.295
DOI:
10.18653/v1/2020.emnlp-main.295
Bibkey:
Cite (ACL):
Ruipeng Jia, Yanan Cao, Hengzhu Tang, Fang Fang, Cong Cao, and Shi Wang. 2020. Neural Extractive Summarization with Hierarchical Attentive Heterogeneous Graph Network. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3622–3631, Online. Association for Computational Linguistics.
Cite (Informal):
Neural Extractive Summarization with Hierarchical Attentive Heterogeneous Graph Network (Jia et al., EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.295.pdf
Video:
 https://slideslive.com/38939335
Data
CNN/Daily Mail