Bayesian query-focused summarization

Published 1 January 2006Open access

Hal Daumé, Daniel Marcu

DOI10.3115/1220175.1220214

Citations262

TL;DR

It is shown that approximate inference in BAYESUM is possible on large data sets and results in a state-of-the-art summarization system, and how B Bayesian summarization can be understood as a justified query expansion technique in the language modeling for IR framework.

Abstract

We present BAYESUM (for "Bayesian summarization"), a model for sentence extraction in query-focused summarization.BAYESUM leverages the common case in which multiple documents are relevant to a single query.Using these documents as reinforcement for query terms, BAYESUM is not afflicted by the paucity of information in short queries.We show that approximate inference in BAYESUM is possible on large data sets and results in a stateof-the-art summarization system.Furthermore, we show how BAYESUM can be understood as a justified query expansion technique in the language modeling for IR framework.

Keywords

Computer Science

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