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Single Arabic Document Summarization Using Natural Language Processing Technique

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Recent Advances in NLP: The Case of Arabic Language

Part of the book series: Studies in Computational Intelligence ((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.

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Author information

Authors and Affiliations

  1. Computer Department, Damietta University, Damietta, Egypt

    Asmaa A. Bialy, Marwa A. Gaheen & Ahmed A. Ewees

  2. Bisha University, Bisha, Kingdom of Saudi Arabia

    R. M. ElEraky & Ahmed A. Ewees

  3. Faculty of Specific Education, Damietta University, Damietta, Egypt

    R. M. ElEraky

  4. Computer Department, Faculty of Specific Education, Mansoura University, Mansoura, Egypt

    A. F. ElGamal

Authors
  1. Asmaa A. Bialy
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  2. Marwa A. Gaheen
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  3. R. M. ElEraky
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  4. A. F. ElGamal
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  5. Ahmed A. Ewees
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Corresponding author

Correspondence to Ahmed A. Ewees .

Editor information

Editors and Affiliations

  1. Faculty of Science, Mathematics Department, Zagazig University, Zagazig, Egypt

    Mohamed Abd Elaziz

  2. School of Computer Science, Wuhan University, Wuhan, China

    Mohammed A. A. Al-qaness

  3. Computer Department, Damietta University, Damietta, Egypt

    Ahmed A. Ewees

  4. School of Computer Science and Technology, Wuhan University of Technology, Wuhan, China

    Abdelghani Dahou

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Bialy, A.A., Gaheen, M.A., ElEraky, R.M., ElGamal, A.F., Ewees, A.A. (2020). Single Arabic Document Summarization Using Natural Language Processing Technique. In: Abd Elaziz, M., Al-qaness, M., Ewees, A., Dahou, A. (eds) Recent Advances in NLP: The Case of Arabic Language. Studies in Computational Intelligence, vol 874. Springer, Cham. https://doi.org/10.1007/978-3-030-34614-0_2

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