Abstract
This paper presents a new multi-document summarization method using weighted similarity between topic and non-negative semantic features to extract meaningful sentences relevant to a given topic. The proposed method decomposes a sentence into the linear combination of sparse non-negative semantic features so that it can represent a sentence as the sum of a few semantic features that are comprehensible intuitively. It can avoid extracting the sentences whose similarities with topic are high but are meaningless by using the weighted similarity measure between the topic and the semantic features. Clustering sentences remove noises so that it can avoid the biased semantics of the documents to be reflected in summaries. Besides, it can enhance the coherence of document summaries by arranging extracted sentences in the order of their rank. The experimental results using DUC data show that the proposed method achieves better performance than the other methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Chin-Yew, L.: ROUGE: A Package for Automatic Evaluation of Summaries. In: Proceedings of Workshop on Text Summarization Branches Out, Post-Conference Workshop of ACL (2004)
Goldstein, J., Mittal, V., Carbonell, J., Kantrowitz, M.: Multi-Document Summarization By Sentence Extraction. In: The Proceeding of the ANLP/NAACL Workshop (2000)
Gong, Y., Liu, X.: Generic Text Summarization Using Relevance Measure and Latent Semantic Analysis. In: Proceeding of ACM SIGIR, pp. 19–25 (2001)
Han, J., Kamber, M.: Data Mining Concepts and Techniques. Morgan Kaufmann, San Francisco (2001)
Hoa, H.D.: Overview of DUC 2005. In: Proceedings of the DUC (2005)
Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization. Nature 401, 788–791 (1999)
Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. In: Advances in Neural Information Processing Systems, vol. 13, pp. 556–562 (2000)
Nomoto, T., Matsumoto, Y.: A New Approach to Unsupervised Text Summarization. In: Proceeding of ACM SIGIR, pp. 26–34 (2001)
Lee, J.H., Part, S., Ahn, C.M.: Automatic Generic Document Summarization Based on Non-negative Matrix Factorization. In: Proceeding of BIS (2007)
Park, S., Lee, J.-H., Ahn, C.-M., Hong, J.S., Chun, S.-J.: Query Based Summarization Using Non-negative Matrix Factorization. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds.) KES 2006. LNCS (LNAI), vol. 4253, pp. 84–89. Springer, Heidelberg (2006)
Park, S., Lee, J.-H., Kim, D.-H., Ahn, C.-M.: Multi-document Summarization Based on Cluster Using Non-negative Matrix Factorization. In: van Leeuwen, J., Italiano, G.F., van der Hoek, W., Meinel, C., Sack, H., Plášil, F. (eds.) SOFSEM 2007. LNCS, vol. 4362, pp. 761–770. Springer, Heidelberg (2007)
Radev, D.R., Hovy, E., Mckeown, K.: Introduction to the Special Issue on Summarization. Computational Linguistics 28, 399–408 (2002)
Ricardo, B.Y., Berthier, R.N.: Modern Information Retrieval. ACM Press, New York (1999)
Sassion, H.: Topic-based Summarization at DUC 2005. In: Proceedings of DUC (2005)
Wild, S., Curry, J., Dougherty, A.: Motivating Non-Negative Matrix Factorizations. In: Proceeding of SIAM ALA (2003)
Xu, W., Liu, X., Gong, Y.: Document Clustering Based On Non-negative Matrix Factorization. In: Proceeding of ACM SIGIR, pp. 267–273 (2003)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Park, S., Lee, JH., Kim, DH., Ahn, CM. (2007). Multi-document Summarization Using Weighted Similarity Between Topic and Clustering-Based Non-negative Semantic Feature. In: Dong, G., Lin, X., Wang, W., Yang, Y., Yu, J.X. (eds) Advances in Data and Web Management. APWeb WAIM 2007 2007. Lecture Notes in Computer Science, vol 4505. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72524-4_14
Download citation
DOI: https://doi.org/10.1007/978-3-540-72524-4_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72483-4
Online ISBN: 978-3-540-72524-4
eBook Packages: Computer ScienceComputer Science (R0)