Query-topic focused web pages summarization

  • Authors:

  • Seung Yeol Yoo;Achim Hoffmann

  • Affiliations:

  • School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia;School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia

  • Venue:
  • PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
  • Year:
  • 2006

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Abstract

We present a novel Web Pages Summarizer ContextSummarizer that groups the givenWeb pages into 'sense-clusters' respecting a user's topical interests. ContextSummarizer constructs then an extractive summary for each sense-cluster. A user's topical interest is described by the user who selects and refines some of the word senses disambiguated within the content contexts of the givenWeb pages. The semantic similarity measures between the contents ofWeb pages/segments/sentences and the user-selected word senses were used to choose the most topically relevant sentences as the extractive summaries referring to a user's topical interest. ContextSummarizer addresses the semantic-alignment problem between the content of a Web page, the user's topical interest, and the extractive summary of the Web page. Our case studies and experimental results showed that our query-topic focused extractive summaries returns more topically relevant sentences for an extractive summary than those produced by existing summarization systems.