Joint Matrix Factorization and Manifold-Ranking for Topic-Focused Multi-Document Summarization

Jiwei Tan, Xiaojun Wan, Jianguo Xiao

Abstract: Manifold-ranking has proved to be an effective method for topic-focused multi-document summarization. As basic manifold-ranking based summarization method constructs the relationships between sentences simply by the bag-of-words cosine similarity, we believe a better similarity metric will further improve the effectiveness of manifold-ranking. In this paper, we propose a joint optimization framework, which integrates the manifold-ranking process with a similarity metric learning process. The joint framework aims at learning better sentence similarity scores and better sentence ranking scores simultaneously. Experiments on DUC datasets show the proposed joint method achieves better performance than the manifold-ranking baselines and several popular methods.