An Experimental Comparison of Supervised and Unsupervised Approaches to Text Summarization

Authors:
Tadashi Nomoto;Yuji Matsumoto
Affiliations:
-;-
Venue:
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Year:
2001

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Abstract

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.