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Extracting main content of a topic on online social network by multi-document summarization

  • Harbin Institute of Technology

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

Abstract

Online social media has become one of the most important ways people communicate, while how to find valuable information from huge amounts of data becomes a key problem. We present a novel topic extraction method that employs topic value of each words and social model attributes as additional features based on the multi-document summarization. The experimental results show that the multi-document summarization with the topic and the sociality are helpful to extract topics from social media.

Original languageEnglish
Title of host publicationProceedings of the 2012 8th International Conference on Computational Intelligence and Security, CIS 2012
PublisherIEEE Computer Society
Pages52-55
Number of pages4
ISBN (Print)9780769548968
DOIs
StatePublished - 2012
Event8th International Conference on Computational Intelligence and Security, CIS 2012 - Guangzhou, Guangdong, China
Duration: 17 Nov 201218 Nov 2012

Publication series

NameProceedings of the 2012 8th International Conference on Computational Intelligence and Security, CIS 2012

Conference

Conference8th International Conference on Computational Intelligence and Security, CIS 2012
Country/TerritoryChina
CityGuangzhou, Guangdong
Period17/11/1218/11/12

Keywords

  • big data
  • media
  • multi-document summarization

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