Pseudo-relevance feedback and statistical query expansion for web snippet generation

Authors:
Youngjoong Ko;Hongkuk An;Jungyun Seo
Affiliations:
Department of Computer Engineering, Dong-A University, 840 Hadan 2-dong, Saha-gu, Busan 604-714, Republic of Korea;Department of Computer Science and Program of Integrated Biotechnology, Sogang University, Sinsu-dong 1, Mapo-gu, Seoul 121-742, Republic of Korea;Department of Computer Science and Program of Integrated Biotechnology, Sogang University, Sinsu-dong 1, Mapo-gu, Seoul 121-742, Republic of Korea
Venue:
Information Processing Letters
Year:
2008

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Abstract

A (page or web) snippet is a document excerpt allowing a user to understand if a document is indeed relevant without accessing it. This paper proposes an effective snippet generation method. A statistical query expansion approach with pseudo-relevance feedback and text summarization techniques are applied to salient sentence extraction for good quality snippets. In the experimental results, the proposed method showed much better performance than other methods including those of commercial Web search engines such as Google and Naver.