Statistical Text Summarization Using a Category-Based Language Model on a Bootstrapping Framework

Abstract

Traditional text summarization systems have not used the category information of documents to be summarized. However, the estimated weights of each word can be often biased on small data such as a single document. Thus we proposed an effective feature-weighting method for document summarization that utilizes category information and solves the biased probability problem. The method uses a category-based smoothing method and a bootstrapping framework. As a result, in our experiments, our proposed summarization method achieves better performance than other statistical sentence-extraction methods.