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

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.