A Novel Feature-based Bayesian Model for Query Focused Multi-document Summarization

2012/12/10 by Jiwei Li, Li, Jiwei, Sujian Li +1
Computer Science · #Advanced Text Analysis Techniques #Topic Modeling #Web Data Mining and Analysis #cs.CL #cs.IR

paper · pdf · doi:10.48550/arxiv.1212.2006

This paper has been withdrawn by the author due to a crucial sign error in equation

arxiv created 2013/12/27 · arxiv updated 2013/12/30

Abstract

Both supervised learning methods and LDA based topic model have been successfully applied in the field of query focused multi-document summarization. In this paper, we propose a novel supervised approach that can incorporate rich sentence features into Bayesian topic models in a principled way, thus taking advantages of both topic model and feature based supervised learning methods. Experiments on TAC2008 and TAC2009 demonstrate the effectiveness of our approach.

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