Volume 12 Number 6 (Nov. 2017)
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JCP 2017 Vol.12(6): 550-563 ISSN: 1796-203X
doi: 10.17706/jcp.12.6.550-563

Extractive Based Automatic Text Summarization

Sagar M. Patel1, Vipul K. Dabhi2, Harshadkumar B. Prajapati2
1Department of Information Technology, Chandubhai S. Patel Institute of Technology (CSPIT), Charotar University of Science and Technology (CHARUSAT), Changa, Gujarat, India.
2Department of Information Technology, Faculty of Technology, Dharmsinh Desai University (DDU), Nadiad, Gujarat, India.

Abstract—Automatic text summarization is the process of reducing the text content and retaining the important points of the document. Generally, there are two approaches for automatic text summarization: Extractive and Abstractive. The process of extractive based text summarization can be divided into two phases: pre-processing and processing. In this paper, we discuss some of the extractive based text summarization approaches used by researchers. We also provide the features for extractive based text summarization process. We also present the available linguistic preprocessing tools with their features, which are used for automatic text summarization. The tools and parameters useful for evaluating the generated summary are also discussed in this paper. Moreover, we explain our proposed lexical chain analysis approach, with sample generated lexical chains, for extractive based automatic text summarization. We also provide the evaluation results of our system generated summary. The proposed lexical chain analysis approach can be used to solve different text mining problems like topic classification, sentiment analysis, and summarization.

Index Terms—Automatic text summarization, extractive, abstractive, linguistic processing, lexical chain analysis, topic classification, sentiment analysis.


Cite: Sagar M. Patel, Vipul K. Dabhi, Harshadkumar B. Prajapati, "Extractive Based Automatic Text Summarization," Journal of Computers vol. 12, no. 6, pp. 550-563, 2017.

General Information

ISSN: 1796-203X
Frequency: Monthly (2006-2014); Bimonthly (Since 2015)
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, CNKI,etc
E-mail: jcp@iap.org
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