Manifold ranking with sink points for update summarization

Abstract

Update summarization aims to create a summary over a topic-related multi-document dataset based on the assumption that the user has already read a set of earlier documents of the same topic. Beyond the problems (i.e., topic relevance, salience, and diversity in extracted information) tackled by topic-focused multi-document summarization, the update summarization must address the novelty problem as well. In this paper, we propose a novel extractive approach based on manifold ranking with sink points for update summarization. Specifically, our approach leverages a manifold ranking process over the sentence manifold to find topic relevant and salient sentences. More important, by introducing the sink points into sentence manifold, the ranking process can further capture the novelty and diversity based on the intrinsic sentence manifold. Therefore, we are able to address the four challenging problems above for update summarization in a unified way. Experiments on benchmarks of TAC are performed and the evaluation results show that our approach can achieve comparative performance to the existing best performing systems in TAC tasks.