Bridging the Gap between Intrinsic and Perceived Relevance in Snippet Generation
Jing He, Pablo Ariel Duboue, Jian‐Yun Nie · 2012
Snippet generation plays an important role in a search engine. Good snippets provide users a good indication on the main content of a search result related to the query and on whether one can find relevant information in it. Previous studies on snippet generation focused on selecting sentences that are related to the query and to the document. However, resulting snippet may look highly relevant while the document itself is not. A missing factor that has not been considered is the consistency between the perceived relevance by the user in reading the snippet and the intrinsic relevance of the document. This factor is important to avoid generating a seemingly relevant snippet for an irrelevant document and vice versa. In this paper, we incorporate this factor in a snippet generation method that imposes the constraint that the snippet of a more relevant document should also be more relevant to the query. We derive a set of pairwise preferences between sentences from relevance judgments. We then use this set to train a gradient boosting decision tree to model a sentence scoring function used in snippet generation. Compared to the existing snippet generation methods and to the snippets generated by a commercial search engine, our snippets are more consistent with the true relevance of the documents. When the snippets are incorporated into a document ranking function, we also observe a significant improvement in retrieval effectiveness. This study shows the importance to generate snippets indicating the right level of relevance to the search results.