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

Authors:
Hajime Morita;Tetsuya Sakai;Manabu Okumura
Affiliations:
Microsoft Research Asia, Beijing, China and Tokyo Institute of Technology, Tokyo, Japan;Microsoft Research Asia, Beijing, China;Precision and Intelligence Laboratory, Tokyo Institute of Technology, Tokyo, Japan
Venue:
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Year:
2011

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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.