WHUSUM: Wuhan University at the Update Summarization Task of TAC 2009

This paper describes the system WHUSUM we developed to participate in the update summarization task of TAC 2009. Given a topic and corresponding topic statement, this year's task is to write 2 summaries (one for Document Set A and one for Document Set B) that meet the information need expressed in the topic statement. In order to generate a topic-oriented summary for Set A, We present a co-training based strategy to select the topic relevant sentences from two abundant views and adopt a graph-based ranking algorithm (i.e. GRASSHOPPER) to achieve both information richness and content diversity in the generated summary. Furthermore, to capture the novel information in Set B and remove the possible redundant information in historical Document Set A, we propose two approaches to encourage novelty. One is to incorporate similarity between sentences in historical set and current set in the prior ranking of GRASSHOPPER. Another is to directly rank sentences for Document Set B first, and then to adjust their ranking scores based on the content comparison between the relevant sentence sets in A and B. The official evaluation results show that our system gets competitive performance in general topic-oriented summarization task and ranks in the middle among 52 submitted systems in update summarization task, which demonstrate that there is still large room to improve the novelty detection mechanism of the system.