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Incorporating Commonsense Knowledge into Abstractive Dialogue Summarization via Heterogeneous Graph Networks

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Abstractive dialogue summarization is the task of capturing the highlights of a dialogue and rewriting them into a concise version. In this paper, we present a novel multi-speaker dialogue summarizer to demonstrate how large-scale commonsense knowledge can facilitate dialogue understanding and summary generation. In detail, we consider utterance and commonsense knowledge as two different types of data and design a Dialogue Heterogeneous Graph Network (D-HGN) for modeling both information. Meanwhile, we also add speakers as heterogeneous nodes to facilitate information flow. Experimental results on the SAMSum dataset show that our model can outperform various methods. We also conduct zero-shot setting experiments on the Argumentative Dialogue Summary Corpus, the results show that our model can better generalized to the new domain.

Original languageEnglish
Title of host publicationChinese Computational Linguistics - 20th China National Conference, CCL 2021, Proceedings
EditorsSheng Li, Maosong Sun, Yang Liu, Hua Wu, Liu Kang, Wanxiang Che, Shizhu He, Gaoqi Rao
PublisherSpringer Science and Business Media Deutschland GmbH
Pages127-142
Number of pages16
ISBN (Print)9783030841850
DOIs
StatePublished - 2021
Externally publishedYes
Event20th China National Conference on Computational Linguistics, CCL 2021 - Virtual, Online
Duration: 13 Aug 202115 Aug 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12869 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th China National Conference on Computational Linguistics, CCL 2021
CityVirtual, Online
Period13/08/2115/08/21

Keywords

  • Commonsense knowledge
  • Dialogue summarization
  • Graph neural networks

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