Abstract
In this paper, we propose a novel multi-document summarization strategy based on Basic Element (BE) vector clustering. In this strategy, sentences are represented by BE vectors instead of word or term vectors before clustering. BE is a head-modifier-relation triple representation of sentence content, and it is more precise to use BE as semantic unit than to use word. The BE-vector clustering is realized by adopting the k-means clustering method, and a novel clustering analysis method is employed to automatically detect the number of clusters, K. The experimental results indicate a superiority of the proposed strategy over the traditional summarization strategy based on word vector clustering. The summaries generated by the proposed strategy achieve a ROUGE-1 score of 0.37291 that is better than those generated by traditional strategy (at 0.36936) on DUC04 task-2.
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Liu, D., He, Y., Ji, D., Yang, H. (2006). Multi-document Summarization Based on BE-Vector Clustering. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2006. Lecture Notes in Computer Science, vol 3878. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11671299_49
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DOI: https://doi.org/10.1007/11671299_49
Publisher Name: Springer, Berlin, Heidelberg
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