Title :
An experimental comparison of supervised and unsupervised approaches to text summarization
Author :
Nomoto, Tadashi ; Matsumoto, Yuji
Author_Institution :
Nat. Inst. of Japanese Literature, Tokyo, Japan
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 unsupervised 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
Keywords :
decision trees; learning (artificial intelligence); pattern clustering; text analysis; C4.5 decision tree algorithm; K-means clustering algorithm; minimum description length principle; supervised text summarization; unsupervised text summarization; Classification tree analysis; Clustering algorithms; Costs; Decision trees; Diversity methods; Humans; Natural languages;
Conference_Titel :
Data Mining, 2001. ICDM 2001, Proceedings IEEE International Conference on
Conference_Location :
San Jose, CA
Print_ISBN :
0-7695-1119-8
DOI :
10.1109/ICDM.2001.989585
