Multi-document Summarization Based on BE-Vector Clustering

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.