An abstract-based approach for text classification

Abstract

Text classification is a supervised learning task for assigning text document to one or more predefined classes/topics. These topics are determined by a set of training documents. In order to construct a classification model, a machine learning algorithm was used. Training data is often a set of full-text documents. The training model is used to predict a class for new coming document. In this paper, we propose a text classification approach based on automatic text summarization. The proposed approach is tested with 2000 Vietnamese text documents downloaded from vnexpress.net and vietnamnet.vn. The experimental results confirm the feasibility of proposed model.