Improving the informativeness of verbose queries using summarization techniques for spoken document retrieval

Query-by-example information retrieval aims at helping users to find relevant documents accurately when users provide specific query exemplars describing what they are interested in. The query exemplars are usually long and in the form of either a partial or even a full document. However, they may contain extraneous terms (or off-topic information) that would have a negative impact on the retrieval performance. In this paper, we propose to integrate extractive summarization techniques into the retrieval process so as to improve the informativeness of a verbose query exemplar. The original query exemplar is first divided into several sub-queries or sentences. To construct a new concise query exemplar, summarization techniques are then employed to select a salient subset of sub-queries. Experiments on the TDT Chinese collection show that the proposed approach is indeed effective and promising.