Topic analysis for topic-focused multi-document summarization

Xiaojun Wan
2009 Proceeding of the 18th ACM conference on Information and knowledge management - CIKM '09  
Topic-focused multi-document summarization aims to produce a summary biased to a given topic or user profile. This paper presents a novel extractive approach based on manifold-ranking of sentences to this summarization task. The manifold-ranking process can naturally make full use of both the relationships among all the sentences in the documents and the relationships between the given topic and the sentences. The ranking score is obtained for each sentence in the manifold-ranking process to
more » ... ote the biased information richness of the sentence. Then the greedy algorithm is employed to impose diversity penalty on each sentence. The summary is produced by choosing the sentences with both high biased information richness and high information novelty. Experiments on DUC2003 and DUC2005 are performed and the ROUGE evaluation results show that the proposed approach can significantly outperform existing approaches of the top performing systems in DUC tasks and baseline approaches.
doi:10.1145/1645953.1646184 dblp:conf/cikm/Wan09 fatcat:hgsvn4lsajht5l5k74mfo3jgdq