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Using Statistical and Semantic Analysis for Arabic Text Summarization

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International Conference on Information Technology and Communication Systems (ITCS 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 640))

Abstract

Automatic text summarization is an essential tool to overcome the problem of information overload. So far this field has not been studied enough for Arabic language and currently only few related works are available. Arabic text summarization is faced with two main issues: how to extract semantic relationships between textual units and deal with redundancy. To overcome these problems, we propose in this paper a hybrid method to generate an extractive summary of Arabic documents. Our approach is based on a two-dimensional undirected and weighted graph with sentences as nodes and each pair of sentences are connected by two edges representing the statistical and semantic similarity measure. The statistical similarity measure builds on the content overlap between two sentences, while the semantic one is based upon semantic information extracted from Arabic WordNet (AWN) ontology. Then, the score of each sentence is computed by performing the ranking algorithm PageRank on the generated graph. Thereafter, the score of each sentence is performed by adding other statistical features of the text such as TF.ISF and sentence position. The final summary is built by selecting the top-ranking sentences. Finally, we deal with redundancy and information diversity issues by using an adapted maximal marginal relevance (MMR) method. Experimental results on EASC dataset show that our proposed approach outperforms some of existing Arabic summarization systems.

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References

  1. Hovy, E.H.: Automated text summarization. In: Mitkov, R. (ed.) The Oxford Handbook of Computational Linguistics, pp. 583–598. Oxford University Press, Oxford (2005)

    Google Scholar 

  2. Luhn, H.P.: The automatic creation of literature abstracts. IBM J. Res. Dev. 2, 159–165 (1958)

    Article  MathSciNet  Google Scholar 

  3. Edmundson, H.P.: New methods in automatic extracting. J. ACM 16(2), 264–285 (1969)

    Article  MATH  Google Scholar 

  4. Douzidia, F.S., Lapalme, G.: Lakhas, an Arabic summarization system. In: Proceedings of 2004 Document Understanding Conference (DUC 2004), Boston, MA (2004)

    Google Scholar 

  5. AlSanie, W.: Towards an infrastructure for Arabic text summarization using rhetorical structure theory. M.Sc. Thesis. King Saud University, Riyadh, Saudi Arabia (2005)

    Google Scholar 

  6. Haboush, A., Momani, A., Al-Zoubi, M., Tarazi, M.: Arabic text summarization model using clustering techniques. World Comput. Sci. Inf. Technol. J. (WCSIT) 2(3), 62–67 (2012). ISSN: 2221-0741

    Google Scholar 

  7. El-Haj, M., Kruschwitz, U., Fox, C.: Exploring clustering for multi-document arabic summarisation. In: Salem, M., Shaalan, K., Oroumchian, F., Shakery, A., Khelalfa, H. (eds.) Information Retrieval Technology. Lecture Notes in Computer Science, vol. 7097, pp. 550–561. Springer, Berlin (2011)

    Chapter  Google Scholar 

  8. Ibrahim, A., Elghazaly, T.: Rhetorical representation and vector representation in summarizing arabic text. In: Métais, E., Meziane, F., Saraee, M., Sugumaran, V., Vadera, S. (eds.) Natural Language Processing and Information Systems. Lecture Notes in Computer Science, vol. 7934, pp. 421–424. Springer, Berlin (2013)

    Chapter  Google Scholar 

  9. Oufaida, H., Nouali, O., Blache, P.: Minimum redundancy and maximum relevance for single and multidocument Arabic text summarization. J King Saud Univ. Comput. Inf. Sci. 26(4), 450–461 (2014). Special Issue on Arabic NLP

    Google Scholar 

  10. El-Haj, M., Kruschwitz, U., Fox, C.: Using mechanical turk to create a corpus of arabic summaries. In: Proceedings of the International Conference on Language Resources and Evaluation (LREC), Valletta, Malta. In the Language Resources (LRs) and Human Language Technologies (HLT) for Semitic Languages Workshop Held in Conjunction with the 7th International Language Resources and Evaluation Conference, pp. 36–39 (2010)

    Google Scholar 

  11. Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)

    Article  Google Scholar 

  12. Ferreira, R., de Souza Cabral, L., Freitas, F., Lins, R.D., de Frana Silva, G., Simske, S.J., Favaro, L.: A multi-document summarization system based on statistics and linguistic treatment. Expert Syst. Appl. 41(13), 5780–5787 (2014)

    Article  Google Scholar 

  13. Pal, A.R., Saha, D.: An approach to automatic text summarization using WordNet. In: Advance Computing Conference (IACC), 2014 IEEE International, pp. 1169–1173 (2014). doi:10.1109/IAdCC.2014.6779492

  14. Estiri, A., Kahani, M., Ghaemi, H., Abasi, M.: Improvement of an abstractive summarization evaluation tool using lexical-semantic relations and weighted syntax tags in Farsi language. In: 2014 Iranian Conference on Intelligent Systems (ICIS), pp. 1–6 (2014). doi:10.1109/IranianCIS.2014.6802594

  15. Patil, A.P., Dalmia, S., Abu Ayub Ansari, S., Aul, T., Bhatnagar, V.: Automatic text summarizer. In: 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1530–1534 (2014). doi:10.1109/ICACCI.2014.6968629

  16. Wei, T., Lu, Y., Chang, H., Zhou, Q., Bao, X.: A semantic approach for text clustering using WordNet and lexical chains. Expert Syst. Appl. 42(4), 2264–2275 (2015). doi:10.1016/j.eswa.2014.10.023. ISSN: 0957-4174

  17. Bouras, C., Tsogkas, V.: A clustering technique for news articles using WordNet. Knowl Based Syst. 36, 115–128 (2012)

    Article  Google Scholar 

  18. Chen, C.L., Tseng, F.S., Liang, T.: An integration of WordNet and fuzzy association rule mining for multi-label document clustering. Data Knowl. Eng. 69(11), 1208–1226 (2010)

    Article  Google Scholar 

  19. Sachdeva, P., Verma, S., Singh, S.K.: An improved approach to word sense disambiguation. In: 2014 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), pp. 000235–000240 (2014)

    Google Scholar 

  20. Dhungana, U.R., Shakya, S., Baral, K., Sharma, B.: Word Sense Disambiguation using WSD specific WordNet of polysemy words. In: 2015 IEEE International Conference on Semantic Computing (ICSC), pp. 148–152 (2015)

    Google Scholar 

  21. Gao, J.B., Zhang, B.W., Chen, X.H.: A WordNet-based semantic similarity measurement combining edge-counting and information content theory. Eng. Appl. Artif. Intell. 39, 80–88 (2015)

    Article  Google Scholar 

  22. Li, Y., Li, H., Cai, Q., Han, D.: A novel semantic similarity measure within sentences. In: 2012 2nd International Conference on Computer Science and Network Technology (ICCSNT), pp. 1176–1179 (2012). doi:10.1109/ICCSNT.2012.6526134

  23. Varelas, G., Voutsakis, E., Raftopoulou, P., Petrakis, E.G.M., Milios, E.E.: Semantic similarity methods in wordnet and their application to information retrieval on the web. In: Proceedings of the Seventh Annual ACM International Workshop on Web Information and Data Management, WIDM 2005, pp. 10–16. ACM, New York (2005)

    Google Scholar 

  24. Khoja, S.: APT: Arabic part-of-speech tagger. In: Proceedings of the Student Workshop at NAACL, pp. 20–25 (2001)

    Google Scholar 

  25. Pedersen, T.: Information content measures of semantic similarity perform better without sense-tagged text. In: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, HLT 2010, pp. 329–332. Association for Computational Linguistics, Stroudsburg (2010)

    Google Scholar 

  26. Wu, Z., Palmer, M.: Verbs semantics and lexical selection. Paper Presented at the Proceedings of the 32nd Annual Meeting on Association for Computational Linguistics (1994)

    Google Scholar 

  27. Lin, D.: An information-theoretic definition of similarity. In: Proceedings of the 15th International Conference on Machine Learning, Madison, WI, pp. 296–304 (1998)

    Google Scholar 

  28. Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Comput. Netw. 30(1–7), 107–117 (1998)

    Google Scholar 

  29. Mihalcea, R., Tarau, P.: TextRank: bringing order into texts. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2004), Barcelona, Spain (2004)

    Google Scholar 

  30. Erkan, G., Radev, D.R.: Lexrank: graph-based lexical centrality as salience in text summarization. J. Artif. Intell. Res. 22, 457–479 (2004)

    Google Scholar 

  31. Alguliyev, R.M., Aliguliyev, R.M., Isazade, N.R.: An unsupervised approach to generating generic summaries of documents. Appl. Soft Comput. 34, 236–250 (2015). ISSN: 1568-4946

    Article  Google Scholar 

  32. The Global Wordnet Association. http://www.globalwordnet.org/

  33. Elberrichi, Z., Abidi, K.: Arabic text categorization: a comparative study of different representation modes. Int. Arab J. Inf. Technol. 9, 465–470 (2012)

    Google Scholar 

  34. Malik, R., Subramaniam, V., Kaushik, S.: Automatically selecting answer templates to respond to customer emails. In: Proceedings of IJCAI 2007, Hyderabad, India, pp. 1659–1664 (2007)

    Google Scholar 

  35. Carbonell, J.G., Goldstein, J.: The use of MMR, diversity-based re-ranking for reordering documents and producing summaries. In: SIGIR, pp. 335–336 (1998)

    Google Scholar 

  36. Alami, N., Meknassi, M., Alaoui Ouatik, S., Ennahnahi, N.: Arabic text summarization based on graph theory. In: IEEE/ACS 12th International Conference of Computer Systems and Applications (AICCSA), Marrakech, pp. 1–8 (2015)

    Google Scholar 

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Authors and Affiliations

  1. Laboratory of Informatics and Modelling, Faculty of Science Dhar EL Mahraz, University Sidi Mohamed Ben Abdellah (USMBA), Fez, Morocco

    Nabil Alami, Yassine El Adlouni, Noureddine En-nahnahi & Mohammed Meknassi

Authors
  1. Nabil Alami
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  2. Yassine El Adlouni
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  3. Noureddine En-nahnahi
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  4. Mohammed Meknassi
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Corresponding author

Correspondence to Nabil Alami .

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Editors and Affiliations

  1. ENSA, Khouribga, Morocco

    Gherabi Noreddine

  2. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

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Alami, N., El Adlouni, Y., En-nahnahi, N., Meknassi, M. (2018). Using Statistical and Semantic Analysis for Arabic Text Summarization. In: Noreddine, G., Kacprzyk, J. (eds) International Conference on Information Technology and Communication Systems. ITCS 2017. Advances in Intelligent Systems and Computing, vol 640. Springer, Cham. https://doi.org/10.1007/978-3-319-64719-7_4

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  • DOI: https://doi.org/10.1007/978-3-319-64719-7_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64718-0

  • Online ISBN: 978-3-319-64719-7

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