Query-Relevant Summarization using FAQs

Query-Relevant Summarization using FAQs Adam Berger Vibhu O. Mittal School of Computer Science Just Research Carnegie Mellon University 4616 Henry Street Pittsburgh, PA 15213 Pittsburgh, PA 15213 aberger@cs.cmu.edu mittal@justresearch.com Abstract 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. 1 Introduction An important distinction in document summarization is between generic summaries, which capture the cen- tral ideas of the document in much the same way that the abstract of this paper was designed to distill its salient points, and query-relevant summaries, which reflect the relevance of a document to a user-specified query. This paper discusses query-relevant summa- rization, sometimes also called “user-focused summa- rization” (Mani and Bloedorn, 1998). Query-relevant summaries are especially important in the “needle(s) in a haystack” document retrieval problem: a user has an information need expressed as a query (What countries export smoked salmon?), and a retrieval system must locate within a large collection of documents those documents most likely to fulfill this need. Many interactive retrieval systems—web search engines like Altavista, for instance—present the user with a small set of candi- date relevant documents, each summarized; the user must then perform a kind of triage to identify likely relevant documents from this set. The web page sum- maries presented by most search engines are generic, not query-relevant, and thus provide very little guid- ance to the user in assessing relevance. Query-relevant summarization (QRS) aims to provide a more effective characterization of a document by accounting for the user’s information need when generating a summary. Search for relevant documents � � � � � � � � � Summarize documents relative to Q σ �  σ       σ       σ  (a) (b) Figure 1: One promising setting for query-relevant sum- marization is large-scale document retrieval. Given a user query ! , search engines typically first (a) identify a set of documents which appear potentially relevant to the query, and then (b) produce a short characterization "$#&%(')!+* of each document’s relevance to ! . The purpose of "$#&%(')!+* is to as- sist the user in finding documents that merit a more detailed inspection. As with almost all previous work on summarization, this paper focuses on the task of extractive summariza- tion: selecting as summaries text spans—either com- plete sentences or paragraphs—from the original doc- ument. 1.1 Statistical models for summarization From a document , and query - , the task of query- relevant summarization is to …

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