Paper: Significant Sentence Extraction by Euclidean Distance Based on Singular Value Decomposition

ACL ID I05-1056
Title Significant Sentence Extraction by Euclidean Distance Based on Singular Value Decomposition
Venue International Joint Conference on Natural Language Processing
Session Main Conference
Year 2005
Authors

This paper describes an automatic summarization approach that constructs a summary by extracting the significant sentences. The approach takes advantage of the cooccurrence relationships between terms only in the document. The techniques used are principal compo- nent analysis (PCA) to extract the significant terms and singular value decompostion (SVD) to find out the significant sentences. The PCA can quantify both the term frequency and term-term relationship in the doc- ument by the eigenvalue-eigenvector pairs. And the sentence-term matrix can be decomposed into the proper dimensional sentence-concentrated and term-concentrated marices which are used for the Euclidean dis- tances between the sentence and term vectors and also removed the noise of variability in term usage by the SVD. ...