Update summarization via graph-based sentence ranking

Abstract—Due to the fast evolution of the information on the Internet, update summarization has received much attention in recent years. It is to summarize an evolutionary document collection at current time supposing the users have read some related previous documents. In this paper, we propose a graph-ranking-based method. It performs constrained reinforcements on a sentence graph, which unifies previous and current documents, to determine the salience of the sentences. The constraints ensure that the most salient sentences in current documents are updates to previous documents. Since this method is NP-hard, we then propose its approximate method, which is polynomial time solvable. Experiments on the TAC 2008 and 2009 benchmark data sets show the effectiveness and efficiency of our method. Index Terms—Summarization, update summarization, topic-focused summarization, multidocument summarization, extraction-based summarization, graph-based ranking, manifold ranking, large-margin constrained ranking, novelty, quadratically constrained quadratic programming, quadratic programming Ç