A Comparative Study of Answer-Contained Snippets and Traditional Snippets

Almost every text search engine uses snippets to help users quickly assess the relevance of retrieved items in the ranked list. Although answer-contained snippets can help to improve the effectiveness of search intuitively, quantitative study of such intuition remains untouched. In this paper, we first propose a simple answer-contained snippet method for community-based Question and Answer (cQA) search, and then compare our method with the state-of-the-art traditional snippet algorithms. The experimental results show that the answer-contained snippet method significantly outperforms the state-of-the-art traditional methods, considering relevance judgements and information satisfaction evaluations.