Coherent narrative summarization with a cognitive model

Renxian Zhang, Wenjie Li, Naishi Liu, Dehong Gao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

5 Citations (Scopus)

Abstract

For summary readers, coherence is no less important than informativeness and is ultimately measured in human terms. Taking a human cognitive perspective, this paper is aimed to generate coherent summaries of narrative text by developing a cognitive model. To model coherence with a cognitive background, we simulate the long-term human memory by building a semantic network from a large corpus like Wiki and design algorithms to account for the information flow among different compartments of human memory. Proposition is the basic processing unit for the model. After processing a whole narrative in a cyclic way, our model supplies information to be used for extractive summarization on the proposition level. Experimental results on two kinds of narrative text, newswire articles and fairy tales, show the superiority of our proposed model to several representative and popular methods.
Original languageEnglish
Pages (from-to)134-160
Number of pages27
JournalComputer Speech and Language
Volume35
DOIs
Publication statusPublished - 27 Jul 2016

Keywords

  • Cognitive modeling
  • Coherence
  • Proposition extraction
  • Summarization

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Human-Computer Interaction

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