Supervised learning methods and LDA based topic model have been successfully applied in the field of 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. Experimental results on DUC2007, TAC2008 and TAC2009 demonstrate the effectiveness of our approach.
REF addressed multi-document summarization by introducing Bayesian topic models.
A Novel Feature-based Bayesian Model for Query Focused Multi-document Summarization
Transactions of the Association for Computational Linguistics
Transactions of the Association for Computational Linguistics
Computer Science