PNR2: Ranking Sentences with Positive and Negative Reinforcement for Query-Oriented Update Summarization

Query-oriented update summarization is an emerging summarization task very recently. It brings new challenges to the sentence ranking algorithms that require not only to locate the important and query-relevant information, but also to capture the new information when document collections evolve. In this paper, we propose a novel graph based sentence ranking algorithm, namely PNR, for update summarization. Inspired by the intuition that “a sentence receives a positive influence from the sentences that correlate to it in the same collection, whereas a sentence receives a negative influence from the sentences that correlates to it in the different (perhaps previously read) collection”, PNR models both the positive and the negative mutual reinforcement in the ranking process. Automatic evaluation on the DUC 2007 data set pilot task demonstrates the effectiveness of the algorithm.