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Single Arabic Document Summarization Using Natural Language Processing Technique

  • Asmaa A. Bialy
  • Marwa A. Gaheen
  • R. M. ElEraky
  • A. F. ElGamal
  • Ahmed A. EweesEmail author
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 874)

Abstract

This paper presents a method based on natural language processing (NLP) for single Arabic document summarization. The suggested method based on the extractive method to select the most valuable information in the document. However, working with Arabic text is considered as a challenging task, this chapter tries to produce an accurate result by using some of NLP techniques. The proposed method is formed from three phases, the first one work as a pre-processing phase to unify synonyms terms, stemming, remove punctuation marks and remove text decoration. Consequently, it produces the features vectors and scores these features to start to select the clauses with the highest scores then marks it as important clauses. The suggested method’s results are compared versus the traditional methods. In this context, two human experts summarized all the datasets manually in order to prepare a strong compare and effective evaluation of the suggested method. In the evaluation phase, some of the performance measures include accuracy, precision, recall, f-measure, and Rouge measure are used. The experimental results denoted that the suggested method showed a competitive execution compared with the human experts in summarization ratio as well as in the accuracy of the produced document.

Keywords

Natural language processing Arabic text summarization Single document summarization Extractive method 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Asmaa A. Bialy
    • 1
  • Marwa A. Gaheen
    • 1
  • R. M. ElEraky
    • 2
    • 3
  • A. F. ElGamal
    • 4
  • Ahmed A. Ewees
    • 1
    • 2
    Email author
  1. 1.Computer DepartmentDamietta UniversityDamiettaEgypt
  2. 2.Bisha UniversityBishaKingdom of Saudi Arabia
  3. 3.Faculty of Specific EducationDamietta UniversityDamiettaEgypt
  4. 4.Computer Department, Faculty of Specific EducationMansoura UniversityMansouraEgypt

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