A Bayesian Method to Incorporate Background Knowledge during Automatic Text Summarization

Annie Louis

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In order to summarize a document, it is often useful to have a background set of documents from the domain to serve as a reference for determining new and important information in the input document. We present a model based on Bayesian surprise which provides an intuitive way to identify surprising information from a summarization input with respect to a background corpus. Specifically, the method quantifies the degree to which pieces of information in the input change one’s beliefs’ about the world represented in the background. We develop systems for generic and update summarization based on this idea. Our method provides competitive content selection performance with particular advantages in the update task where systems are given a small and topical background corpus.
Original languageEnglish
Title of host publicationProceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Place of PublicationBaltimore, Maryland
PublisherAssociation for Computational Linguistics
Number of pages6
Publication statusPublished - 1 Jun 2014

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