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An Arabic Multi-source News Corpus: Experimenting on Single-document Extractive Summarization

  • Research Article-Computer Engineering and Computer Science
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Abstract

Automatic text summarization is considered as an important task in various fields in natural language processing such as information retrieval. It is a process of automatically generating a text representation. Text summarization can be a solution to the problem of information overload. Hence, with the large amount of information available on the Internet, the presentation of a document by a summary helps to get the most relevant result of a search. We propose in this paper a new free Arabic structured corpus in the standard XML TREC format. ANT corpus v2.1 is collected using RSS feeds from different news sources. This corpus is useful for multiple text mining purposes such as generic text summarization, clustering or classification. We test this corpus for an unsupervised single-document extractive summarization using statistical and graph-based language-independent summarizers such as LexRank, TextRank, Luhn and LSA. We investigate the sensitivity of the summarization process to the stemming and stop words removal steps. We evaluate these summarizers performance by comparing the extracted texts fragments to the abstracts existing in ANT corpus v2.1 using ROUGE and BLEU metrics. Experimental results show that LexRank summarizer has achieved the best scores for the ROUGE metric using the stop words removal scenario.

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Notes

  1. https://duc.nist.gov/.

  2. MSE 2005: Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization Workshop at the Annual Meeting of the Association of Computational Linguistics (ACL 2005).

  3. MSE 2006: Multilingual Summarization Evaluation at the 21st International Conference on Computational Linguistics (ACL 2006)/44th Annual Meeting of the Association for Computational Linguistics.

  4. http://www.nist.gov/tac/2011/Summarization/index.html.

  5. http://multiling.iit.demokritos.gr/pages/view/662/multiling-2013.

  6. http://www.mturk.com.

  7. http://translate.google.com.

  8. https://github.com/antcorpus/RSSCrawlerArabicCorpus.

  9. https://www.jawharafm.net/ar/.

  10. https://antcorpus.github.io/.

  11. http://www.alarabiya.net/ar/.

  12. http://www.bbc.com/arabic/.

  13. https://arabic.cnn.com/.

  14. http://www.france24.com/ar/.

  15. http://skynewsarabia.com/.

  16. https://pypi.org/project/sumy/.

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Acknowledgements

This work was funded by Emirates College of Technology in Abu Dhabi (UAE) under research Grant IRG-BIT-002-2020. The authors would like to thank the editors and the anonymous reviewers for their relevant comments and suggestions, which significantly enhanced the quality of this paper during the reviewing process.

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Authors and Affiliations

  1. RIADI Research Laboratory, ENSI, Manouba University, Manouba, Tunisia

    Amina Chouigui, Oussama Ben Khiroun & Bilel Elayeb

  2. Faculty of Economics and Management of Nabeul, University of Carthage, Tunis, Tunisia

    Oussama Ben Khiroun

  3. Emirates College of Technology, Abu Dhabi, United Arab Emirates

    Bilel Elayeb

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  1. Amina Chouigui
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  2. Oussama Ben Khiroun
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  3. Bilel Elayeb
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Correspondence to Bilel Elayeb.

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Chouigui, A., Ben Khiroun, O. & Elayeb, B. An Arabic Multi-source News Corpus: Experimenting on Single-document Extractive Summarization. Arab J Sci Eng 46, 3925–3938 (2021). https://doi.org/10.1007/s13369-020-05258-z

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