Bayesian Query-Focused Summarization

Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the ACL, pages 305–312, Sydney, July 2006. c⃝2006 Association for Computational Linguistics Bayesian Query-Focused Summarization Hal Daum´e III and Daniel Marcu Information Sciences Institute 4676 Admiralty Way, Suite 1001 Marina del Rey, CA 90292 me@hal3.name,marcu@isi.edu Abstract We present BAYESUM (for “Bayesian summarization”), a model for sentence ex- traction in query-focused summarization. BAYESUM leverages the common case in which multiple documents are relevant to a single query. Using these documents as re- inforcement for query terms, BAYESUM is not afflicted by the paucity of information in short queries. We show that approxi- mate inference in BAYESUM is possible on large data sets and results in a state- of-the-art summarization system. Further- more, we show how BAYESUM can be understood as a justified query expansion technique in the language modeling for IR framework. 1 Introduction We describe BAYESUM, an algorithm for perform- ing query-focused summarization in the common case that there are many relevant documents for a given query. Given a query and a collection of rel- evant documents, our algorithm functions by ask- ing itself the following question: what is it about these relevant documents that differentiates them from the non-relevant documents? BAYESUM can be seen as providing a statistical formulation of this exact question. The key requirement of BAYESUM is that mul- tiple relevant documents are known for the query in question. This is not a severe limitation. In two well-studied problems, it is the de-facto standard. In standard multidocument summarization (with or without a query), we have access to known rel- evant documents for some user need. Similarly, in the case of a web-search application, an underly- ing IR engine will retrieve multiple (presumably) relevant documents for a given query. For both of these tasks, BAYESUM performs well, even when the underlying retrieval model is noisy. The idea of leveraging known relevant docu- ments is known as query expansion in the informa- tion retrieval community, where it has been shown to be successful in ad hoc retrieval tasks. Viewed from the perspective of IR, our work can be inter- preted in two ways. First, it can be seen as an ap- plication of query expansion to the summarization task (or, in IR terminology, passage retrieval); see (Liu and Croft, 2002; Murdock and Croft, 2005). Second, and more importantly, it can be seen as a method for query expansion in a non-ad-hoc man- ner. That is, BAYESUM is a statistically justified query expansion method in the language modeling for IR framework (Ponte and Croft, 1998). 2 Bayesian Query-Focused Summarization In this section, we describe our Bayesian query- focused summarization model (BAYESUM). This task is very similar to the standard ad-hoc IR task, with the important distinction that we are compar- ing query models against sentence models, rather than against document models. The shortness of sentences means that one must do a good job of creating the query models. To maintain generality, so that our model is …

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