Bayesian query-focused summarization

TitleBayesian query-focused summarization
Publication TypeConference Papers
Year of Publication2006
AuthorsDaumé H, Marcu D
Conference NameProceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Date Published2006///
PublisherAssociation for Computational Linguistics
Conference LocationStroudsburg, PA, USA

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 state-of-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.