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Automatic Arabic text summarization: a survey

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Abstract

This survey investigates several research studies that have been conducted in the field of Arabic text summarization. Specifically, it addresses summarization and evaluation methods, as well as the corpora used in those studies. The literature in this field is fairly limited and relatively new compared to the available literature on other languages, such as English. Therefore, there exists a great opportunity for further research in Arabic text summarization. In addition, one of the largest problems in Arabic summarization was the absence of Arabic gold standard summaries, although this situation is beginning to change, especially with the inclusion of Arabic language as a part of the corpora and tasks in the TAC 2011 MultiLing Pilot and ACL 2013 MultiLing Workshop. Finally, providing the required corpora and adopting them in Arabic summarization studies is an essential demand.

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Notes

  1. http://www.systransoft.com.

  2. http://www.nlpir.nist.gov/related_projects/tipster_summac.

  3. http://www.nlpir.nist.gov/projects/duc/index.html.

  4. http://www.nist.gov/tac.

  5. http://www.nist.gov.

  6. http://www.aclweb.org.

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

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

  9. http://acl2013.org/site.

  10. http://multiling.iit.demokritos.gr/pages/view/1516/multiling-2015.

  11. http://www.mturk.com.

  12. http://www.wikipedia.com.

  13. http://translate.google.com.

  14. http://www.berouge.com.

  15. http://news.google.com.

  16. https://twitter.com.

  17. http://catalog.ldc.upenn.edu/LDC2003T12.

  18. https://www.ldc.upenn.edu.

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Acknowledgments

This work was supported by the Research Center of College of Computer and Information Sciences, King Saud University. The authors are grateful for this support. Also, the authors would like to thank the anonymous reviewers for their constructive comments.

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  1. Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh, 11543, Saudi Arabia

    Asma Bader Al-Saleh & Mohamed El Bachir Menai

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  1. Asma Bader Al-Saleh
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  2. Mohamed El Bachir Menai
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Correspondence to Asma Bader Al-Saleh.

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Al-Saleh, A.B., Menai, M.E.B. Automatic Arabic text summarization: a survey. Artif Intell Rev 45, 203–234 (2016). https://doi.org/10.1007/s10462-015-9442-x

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