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Statistical Features for Extractive Automatic Text Summarization

Statistical Features for Extractive Automatic Text Summarization

Yogesh Kumar Meena, Dinesh Gopalani
Copyright: © 2016 |Pages: 19
ISBN13: 9781522502937|ISBN10: 1522502939|EISBN13: 9781522502944
DOI: 10.4018/978-1-5225-0293-7.ch008
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MLA

Meena, Yogesh Kumar, and Dinesh Gopalani. "Statistical Features for Extractive Automatic Text Summarization." Enterprise Big Data Engineering, Analytics, and Management, edited by Martin Atzmueller, et al., IGI Global, 2016, pp. 126-144. https://doi.org/10.4018/978-1-5225-0293-7.ch008

APA

Meena, Y. K. & Gopalani, D. (2016). Statistical Features for Extractive Automatic Text Summarization. In M. Atzmueller, S. Oussena, & T. Roth-Berghofer (Eds.), Enterprise Big Data Engineering, Analytics, and Management (pp. 126-144). IGI Global. https://doi.org/10.4018/978-1-5225-0293-7.ch008

Chicago

Meena, Yogesh Kumar, and Dinesh Gopalani. "Statistical Features for Extractive Automatic Text Summarization." In Enterprise Big Data Engineering, Analytics, and Management, edited by Martin Atzmueller, Samia Oussena, and Thomas Roth-Berghofer, 126-144. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-5225-0293-7.ch008

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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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