Document summarization via self-present sentence relevance model

Xiaodong Li, Shanfeng Zhu, Haoran Xie, Qing Li

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

6 Citations (Scopus)


Automatic document summarization is always attractive to computer science researchers. A novel approach is proposed to address this topic and mainly focuses on the summarization of plain documents. Conventional summarization methods do not fully use the inter-sentence relevance that is not preserved during the processing. In contrast, to tackle the problem and incorporate the latent relations among sentences, our approach constructs relevance structures at sentence-level for plain documents and each sentence is scored with a significance value. Accordingly, important sentences "present" themselves automatically, and the summary paragraph is then generated by selecting top-k scored sentences. Convergence of the algorithm is proved, and experiment, which is conducted on two data sets (DUC 2006 and DUC 2007), shows that the proposed model gives convincing results.

Original languageEnglish
Title of host publicationDatabase Systems for Advanced Applications - 18th International Conference, DASFAA 2013, Proceedings
Number of pages15
EditionPART 2
Publication statusPublished - 1 Dec 2013
Externally publishedYes
Event18th International Conference on Database Systems for Advanced Applications, DASFAA 2013 - Wuhan, China
Duration: 22 Apr 201325 Apr 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume7826 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference18th International Conference on Database Systems for Advanced Applications, DASFAA 2013


  • Sentence relevance
  • Summarization

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


Dive into the research topics of 'Document summarization via self-present sentence relevance model'. Together they form a unique fingerprint.

Cite this