Statistical Features for Extractive Automatic Text Summarization

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Author(s): Yogesh Kumar Meena (MNIT Jaipur, India)and Dinesh Gopalani (MNIT Jaipur, India)
Copyright: 2020
Pages: 19
Source title: Natural Language Processing: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-0951-7.ch030

Keywords: Artificial Intelligence / Engineering Science Reference / Knowledge Management and Library Science / Natural Language Processing

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Abstract

Automatic Text Summarization (ATS) enables users to save their precious time to retrieve their relevant information need while searching voluminous big data. Text summaries are sensitive to scoring methods, as most of the methods requires to weight features for sentence scoring. In this chapter, various statistical features proposed by researchers for extractive automatic text summarization are explored. Features that perform well are termed as best features using ROUGE evaluation measures and used for creating feature combinations. After that, best performing feature combinations are identified. Performance evaluation of best performing feature combinations on short, medium and large size documents is also conducted using same ROUGE performance measures.

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