Content coverage maximization on word networks for hierarchical topic summarization

Chi Wang, Xiao Yu, Yanen Li, Chengxiang Zhai, Jiawei Han

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper studies text summarization by extracting hierarchical topics from a given collection of documents. We propose a new approach of text modeling via network analysis. We convert documents into a word influence network, and find the words summarizing the major topics with an efficient influence maximization algorithm. Besides, the influence capability of the topic words on other words in the network reveal the relations among the topic words. Then we cluster the words and build hierarchies for the topics. Experiments on large collections of Web documents show that a simple method based on the influence analysis is effective, compared with existing generative topic modeling and random walk based ranking. Copyright is held by the owner/author(s).

Original languageEnglish (US)
Title of host publicationCIKM 2013 - Proceedings of the 22nd ACM International Conference on Information and Knowledge Management
Pages249-258
Number of pages10
DOIs
StatePublished - Dec 11 2013
Event22nd ACM International Conference on Information and Knowledge Management, CIKM 2013 - San Francisco, CA, United States
Duration: Oct 27 2013Nov 1 2013

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Other

Other22nd ACM International Conference on Information and Knowledge Management, CIKM 2013
CountryUnited States
CitySan Francisco, CA
Period10/27/1311/1/13

Keywords

  • Information coverage
  • Keyword extraction
  • Text summarization
  • Topic hierarchy

ASJC Scopus subject areas

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

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