Paper: Query-Relevant Summarization Using FAQs

ACL ID P00-1038
Title Query-Relevant Summarization Using FAQs
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2000

This paper introduces a statistical model for query-relevant summarization: succinctly characterizing the relevance of a document to a query. Learning parameter values for the proposed model requires a large collec- tion of summarized documents, which we do not have, but as a proxy, we use a col- lection of FAQ (frequently-asked question) documents. Taking a learning approach en- ables a principled, quantitative evaluation of the proposed system, and the results of some initial experiments—on a collection of Usenet FAQs and on a FAQ-like set of customer-submitted questions to several large retail companies—suggest the plausi- bility of learning for summarization.