Enhancing web search by using query-based clusters and multi-document summaries

Current web search engines, such as Google, Bing, and Yahoo!, rank the set of documents SD retrieved in response to a user query and display each document D in SD with a title and a snippet, which serves as an abstract of D. Snippets, however, are not as useful as they are designed for, i.e., assisting its users to quickly identify results of interest, if they exist. These snippets are inadequate in providing distinct information and capturing the main contents of the corresponding documents. Moreover, when the intended information need specified in a search query is ambiguous, it is very difficult, if not impossible, for a search engine to identify precisely the set of documents that satisfy the user’s intended request without requiring additional inputs. Furthermore, a document title is not always a good indicator of the content of the corresponding document. All of these design problems can be solved by our proposed query-based cluster and summarizer, called $$Q_{Sum}$$QSum. $$Q_{Sum}$$QSum generates a concise/comprehensive summary for each cluster of documents retrieved in response to a user query, which saves the user’s time and effort in searching for specific information of interest without having to browse through the documents one by one. Experimental results show that $$Q_{Sum}$$QSum is effective and efficient in generating a high-quality summary for each cluster of documents on a specific topic.