基于基本要素向量空间的英文多文档自动摘要  

English Multi-document Summarization Based on Basic Element Vector Space

作　　者：刘德喜[1] 何炎祥[2] 姬东鸿[3] 杨华[2] 

机构地区：[1]襄樊学院物理学系,襄樊441053 [2]武汉大学计算机学院,武汉430079 [3]新加坡信息通讯研究所,新加坡119613

出　　处：《计算机工程》2007年第14期166-167,170,共3页Computer Engineering

基　　金：国家自然科学基金资助重大项目(90104005)

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。This paper proposes a novel multi-document sulmmarization 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.

关 键 词：多文档自动文摘 基本要素 K-均值聚类 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]