Sub-Topic Segmentation in Multi-Document

Abstract:

The similar sentences in multi-document set are combined into one class, and each class is one sub-topic. Describing the sub-topics from the perspective of understanding makes the multi-document summarization become the one with greater coverage and less redundancy. This paper presents a sub-topic segmentation method based on maximum tree algorithm. And based on sentences similarity matrix, maximum tree is calculated, as well as the sub-topic segmentation is realized through the analysis of the different communities for the sub-topic. The experiment shows that the method achieves the desired result.

• Periodical: Advanced Materials Research (Volumes 756-759)
• Pages: 2958-2961
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.756-759.2958
• Online since: September 2013
• Authors: Xiao Yan Yun, Wei Teng
• Keywords: Maximum Tree Algorithm, Multi-Document Summarization, Sub-Topic Segmentation

References

[1] R. Radev, Hongyan Jing, Malgorzata Budzikowska, Centroid-based summarization of multiple documents: sentence extraction, utility-based evaluation, and user studies. In ANLP/NAACL 2000 Workshop, April 2000: 21-29.

DOI: 10.3115/1567564.1567567

[2] Chin-Yew Lin, Eduarad Hovy . From Single to Multi-document Summarization: A Prototype system and its Evaluation. In proceeding of the 40tth anniversary meeting of the association for computational linguistics(ACL-02), Philadephia, USA, 2002: 25-34.

DOI: 10.3115/1073083.1073160

[3] Dragomir R. radev, Kathleen R. Mckeovwn. Generating Natural Languages Summaries from multiple on-line Sources. Computational Linguistics. 1998, 24(3): 21-29.

[4] Sanda Harabagiu, Steven Maiorano. Multi-document summarization with GISTexter Proceedings of the third LREC Conference 2002(LREC 2002), June 2002, Canary Islands, Spain.

[5] E. Filatova,V. Hatzivassiloglou, Event-based Extractive Summarization. In the proceedings of ACL Workshop on Summarization, Barcelona, Span, July (2004).

[6] Endre Boros, Paul B. Kantor, David J. Neu. A Clustering Based Approach to Creating Multi-Document Summaries. In Proceedings of the 24th Annual International ACM SIGIR Conference on Reseach and Development in information retrieval , New Orleans, LA, (2001).

[7] Pascale Fumg, Grace Ngai. Combining Optimal Clustering and Hidden Markov Model for Extractive Summarization. Proceedings of the ACL 2003 workshop on multilingual summarization and question answering . 2003: 21-28.

DOI: 10.3115/1119312.1119315

[8] Qin bing, liu ting, gao ye Identification of logical topic of multi-document set.

[9] Mei jiaju Corpus annotation[M] Shanghai Lexicographical Publishing Bureau, (1983).

[10] Tang ming zhu, Zhang yuan ping, Yang jia, Method of text fuzzy clustering based on concept similarity Science technology and engineering Vol 7, no5.