An Experimental Comparison of Supervised and Unsupervised Approaches to Text Summarization

	This Article
	Subscribers, please Login Purchase article: $19 PDF IEEE Xplore Subscribers RSS feed
	Share
	Email this Article to a friend
	Bibliographic References
	ASCII Text BibTex RefWorks Procite/RefMan
	Add to:
	Digg Furl Spurl Blink Simpy Google Del.icio.us Y!MyWeb
	Search
	Similar Articles Articles by Tadashi Nomoto Articles by Yuji Matsumoto

First IEEE International Conference on Data Mining (ICDM'01)

San Jose, California

November 29-December 02

ISBN: 0-7695-1119-8

	ASCII Text	x
	Tadashi Nomoto, Yuji Matsumoto, "An Experimental Comparison of Supervised and Unsupervised Approaches to Text Summarization," Data Mining, IEEE International Conference on, pp. 630, First IEEE International Conference on Data Mining (ICDM'01), 2001.

	BibTex	x
	@article{ 10.1109/ICDM.2001.989585, author = {Tadashi Nomoto and Yuji Matsumoto}, title = {An Experimental Comparison of Supervised and Unsupervised Approaches to Text Summarization}, journal ={Data Mining, IEEE International Conference on}, volume = {0}, year = {2001}, isbn = {0-7695-1119-8}, pages = {630}, doi = {http://doi.ieeecomputersociety.org/10.1109/ICDM.2001.989585}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Data Mining, IEEE International Conference on TI - An Experimental Comparison of Supervised and Unsupervised Approaches to Text Summarization SN - 0-7695-1119-8 SP EP A1 - Tadashi Nomoto, A1 - Yuji Matsumoto, PY - 2001 VL - 0 JA - Data Mining, IEEE International Conference on ER -

Tadashi Nomoto

Yuji Matsumoto

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDM.2001.989585

The paper presents a direct comparison of supervised and unsupervised approaches to text summarization. As a representative supervised method, we use the C4.5 decision tree algorithm, extended with the Minimum Description Length Principle (MDL), and compare it against several unsupervised methods. It is found that a particular un-supervised method based on an extension of the K-means clustering algorithm, performs equal to and in some cases superior to the decision tree based method.

Citation:

Tadashi Nomoto, Yuji Matsumoto, "An Experimental Comparison of Supervised and Unsupervised Approaches to Text Summarization," icdm, pp.630, First IEEE International Conference on Data Mining (ICDM'01), 2001

Peer Review Notice | Give Us Feedback

Usage of this product signifies your acceptance of the Terms of Use.