摘要： 在基于基本要素(BE)向量空间的英文多文档自动文摘中，句子不再用术语向量或词向量来表达，而是用基本要素向量来表示。在用k-均值聚类算法时，采用一种自动探测k值的技术。实验表明，基于基本要素的多文档自动文摘MSBEC比基于词更优越。

关键词: 多文档自动文摘, 基本要素, k-均值聚类

Abstract: This paper proposes 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. 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.

Key words: multi-document summarization, basic element, k-means clustering

中图分类号: 

• TP391