LDA-based topic formation and topic-sentence reinforcement for graph-based multi-document summarization

Dehong Gao, Wenjie Li, You Ouyang, Renxian Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

6 Citations (Scopus)


In recent years graph-based ranking algorithms have attracted much attention in document summarization. This paper introduces our recent work on applying a topic model, namely LDA, in graph-based summarization. In the proposed approach, LDA is used to automatically identify a set of semantic topics from the documents to be summarized. The identified topics are then used to construct a bipartite graph to represent the documents. Topic-sentence reinforcement is implemented to calculate the salience scores of topics and sentences simultaneously. By incorporating the information embedded in the topics, the sentence ranking result can be improved. Experiments are conducted on the DUC 2004 data set to evaluate the effectiveness of the proposed approach.
Original languageEnglish
Title of host publicationInformation Retrieval Technology - 8th Asia Information Retrieval Societies Conference, AIRS 2012, Proceedings
Number of pages10
Publication statusPublished - 31 Dec 2012
Event8th Asia Information Retrieval Societies Conference, AIRS 2012 - Tianjin, China
Duration: 17 Dec 201219 Dec 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7675 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference8th Asia Information Retrieval Societies Conference, AIRS 2012


  • Graph-based sentence ranking
  • Latent Dirichlet Allocation
  • Multi-document summarization

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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