Graph-Based Marginal Ranking for Update Summarization

Abstract

Update summarization is to summarize a document collection B given that the users have already read another document collection A, which has time stamp prior to that of B. An important and challenging issue in update summarization is that contents in B already covered by A should be excluded from the update summary. In this paper, we propose a graphbased regularization framework MarginRank for update summarization. MarginRank extends the cost function of Zhou’s Manifold Ranking with suppression terms, suppression of A on B, to fulfil the assumption that users have read A. MarginRank ranks sentences in B in a way that the top ranked sentences are most important and at the same time cover different contents from A. Experiments on the benchmark data sets TAC 2008 and 2009 show the effectiveness of the proposed method.