Text Summarization using PCA and SVD


The KIPS Transactions:PartB , Vol. 10, No. 7, pp. 725-734, Dec. 2003
10.3745/KIPSTB.2003.10.7.725,   PDF Download:

Abstract

In this paper, we propose the text summarization method using PCA (Principal Component Analysis) and SVD (Singular Value Decomposition). The proposed method presents a summary by extracting significant sentences based on the distances between thematic words and sentences. To extract thematic words, we use both word freqeuncy and co-occurence information that result from performing PCA. To extract significant sentences, we exploit Euclidean distances between thematic word vectors and sentence vectors that result from carrying out SVD. Experimental results using newspaper articles show that the proposed method is superior to the method using either word frequency or only PCA.


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Cite this article
[IEEE Style]
L. C. Beom, K. M. Su, B. J. Seon, P. H. Lo, "Text Summarization using PCA and SVD," The KIPS Transactions:PartB , vol. 10, no. 7, pp. 725-734, 2003. DOI: 10.3745/KIPSTB.2003.10.7.725.

[ACM Style]
Lee Chang Beom, Kim Min Su, Baeg Jang Seon, and Park Hyeog Lo. 2003. Text Summarization using PCA and SVD. The KIPS Transactions:PartB , 10, 7, (2003), 725-734. DOI: 10.3745/KIPSTB.2003.10.7.725.