2011 IEEE 11th International Conference on Data Mining (2011)
Dec. 11, 2011 to Dec. 14, 2011
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2011.140
Topic-focused multi-document summarization aims to produce a summary given a specific topic description and a set of related documents. It has become a crucial text processing task in many real applications that can help users consume the massive information. This paper presents a novel extractive approach based on supervised lazy random walk (Super Lazy). This approach naturally combines the rich features of sentences with the intrinsic sentence graph structure in a principled way, and thus enjoys the advantages of both the existing supervised and unsupervised approaches. Moreover, our approach can achieve the three major goals of topic-focused multi-document summarization (i.e. relevance, salience and diversity) simultaneously with a unified ranking process. Experiments on the benchmark dataset TAC2008 and TAC2009 are performed and the ROUGE evaluation results demonstrate that our approach can significantly outperform both the state-of-the-art supervised and unsupervised methods.
supervised lazy random walk, topic-focused multi-document summarization, relevance, salience, diversity
J. Guo, X. Cheng and P. Du, "Supervised Lazy Random Walk for Topic-Focused Multi-document Summarization," 2011 IEEE 11th International Conference on Data Mining(ICDM), Vancouver, Canada, 2011, pp. 1026-1031.