How to Improve Text Summarization and Classification by Mutual Cooperation on an Integrated Framework. (30th October 2016)

• Record Type: Journal Article
• Authors: Jeong, Hyoungil; Ko, Youngjoong; Seo, Jungyun
• Is Part Of: Expert systems with applications. Volume 60(2016)
• Page Start: 222
• Page End: 233
• Publication Date: 2016-10-30
• Subjects: Text summarization -- Text classification -- Binary independence model -- Cluster-based language model -- Support vector machine; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2016.05.001
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: An effective integrated framework using both of summary and category information. The summarization technique utilizes the category information from classification. The classification technique utilizes the summary information from summarization. This integrated framework achieves significant improvement. Abstract: Text summarization and classification are core techniques to analyze a huge amount of text data in the big data environment. Moreover, as the need to read texts on smart phones, tablets and television as well as personal computers continues to grow, text summarization and classification techniques become more important and both of them do essential processes for text analysis in many applications. Traditional text summarization and classification techniques have individually been considered as different research fields in this literature. However, we find out that they can help each other as text summarization makes use of category information from text classification and text classification does summary information from text summarization. Therefore, we propose an effective integrated learning framework using both of summary and category information in this paper. In this framework, the feature-weighting method for text summarization utilizes a language model to combine feature distributions in each category and text, and one for text classification does the sentence importance scores estimated from the text summarization. In the experiments, the performances of the integrated framework are better than ones of individual text summarization and classification. In addition, the framework has some advantages of easy implementation and language independence because it is based on only simple statistical approaches and POS tagger.