Extractive Summarization Considering Discourse and Coreference Relations based on Heterogeneous Graph

Yin Jou Huang, Sadao Kurohashi


Abstract
Modeling the relations between text spans in a document is a crucial yet challenging problem for extractive summarization. Various kinds of relations exist among text spans of different granularity, such as discourse relations between elementary discourse units and coreference relations between phrase mentions. In this paper, we propose a heterogeneous graph based model for extractive summarization that incorporates both discourse and coreference relations. The heterogeneous graph contains three types of nodes, each corresponds to text spans of different granularity. Experimental results on a benchmark summarization dataset verify the effectiveness of our proposed method.
Anthology ID:
2021.eacl-main.265
Volume:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Month:
April
Year:
2021
Address:
Online
Editors:
Paola Merlo, Jorg Tiedemann, Reut Tsarfaty
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3046–3052
Language:
URL:
https://aclanthology.org/2021.eacl-main.265
DOI:
10.18653/v1/2021.eacl-main.265
Bibkey:
Cite (ACL):
Yin Jou Huang and Sadao Kurohashi. 2021. Extractive Summarization Considering Discourse and Coreference Relations based on Heterogeneous Graph. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 3046–3052, Online. Association for Computational Linguistics.
Cite (Informal):
Extractive Summarization Considering Discourse and Coreference Relations based on Heterogeneous Graph (Huang & Kurohashi, EACL 2021)
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PDF:
https://aclanthology.org/2021.eacl-main.265.pdf