Automatic Text Categorization using the Importance of Sentences

Automatic text categorization is a problem of automatically assigning text documents to predefined categories. In order to classify text documents, we must extract good features from them. In previous research, a text document is commonly represented by the term frequency and the inverted document frequency of each feature. Since there is a difference between important sentences and unimportant sentences in a document, the features from more important sentences should be considered more than other features. In this paper, we measure the importance of sentences using text summarization techniques. Then a document is represented as a vector of features with different weights according to the importance of each sentence. To verify our new method, we conducted experiments on two language newsgroup data sets: one written by English and the other written by Korean. Four kinds of classifiers were used in our experiments: Na

• Type: Journal
• Year: 2002
• Where: COLING
• Authors: Youngjoong Ko, Jinwoo Park, Jungyun Seo
• Keywords: COLING 2002, COLING 2008, Document, Important Sentences, Text Document