Summarizing relevant information for question-answering using hybrid relevance analysis and surface feature salience

Jen Yuan Yeh, Hao-Ren Ke, Wei Pang Yang

Research output: Contribution to journalArticle

Abstract

Much research for question-answering aims to answer factiod, definitional and biographical questions. In most cases, the answers are given as a name, date, quantity, and so on. In this paper, we try to merge techniques of multidocument summarization and question-answering to generate a brief, well-organized fluent summary to provide more relevant information for the purpose of answering real-world complicated questions. The problem is addressed as a query-biased sentence retrieval task. We propose a hybrid relevance analysis to evaluate the relevance of a sentence to the query. The summary is created by including sentences with the topmost significances which are measured in terms of sentence relevance and surface feature salience. In addition, a modified Maximal Marginal Relevance is proposed for anti-redundancy. The proposed approach was evaluated with the DUC 2005 corpus and found to perform well with competitive results.

Original languageEnglish
Pages (from-to)2549-2556
Number of pages8
JournalWSEAS Transactions on Information Science and Applications
Volume3
Issue number12
Publication statusPublished - 2006 Dec 1

Keywords

  • Hybrid relevance analysis
  • Latent semantic analysis
  • Modified maximal marginal relevance
  • Query-focused summarization
  • Sentence feature salience

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications

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