Query snowball: A co-occurrence-based approach to multi-document summarization for question answering

Hajime Morita, Tetsuya Sakai, Manabu Okumura

Research output: Contribution to journalArticle

Abstract

We propose a new method for query-oriented extractive multi-document summarization. To enrich the information need representation of a given query, we build a co-occurrence graph to obtain words that augment the original query terms. We then formulate the summarization problem as a Maximum Coverage Problem with Knapsack Constraints based on word pairs rather than single words. Our experiments with the NTCIR ACLIA question answering test collections show that our method achieves a pyramid F3-score of up to 0.313, a 36% improvement over a baseline using Maximal Marginal Relevance.

Original languageEnglish
Pages (from-to)124-129
Number of pages6
JournalIPSJ Online Transactions
Volume5
Issue number2012
DOIs
Publication statusPublished - 2012
Externally publishedYes

Keywords

  • Information need representation
  • Multi-document summarization
  • Query-oriented
  • Question answering

ASJC Scopus subject areas

  • Computer Science(all)

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