Developing a new approach to summarize Arabic text automatically using syntactic and semantic analysis

  • Authors

    • Amal Alkhudari University of Kalamoon
    2020-04-03
    https://doi.org/10.14419/ijet.v9i2.30324
  • Abstractive Summarization, Ontology, Semantic Similarity, Syntactic Analysis, Word Sense Disambiguation.
  • Abstract

    Due to the wide spread information and the diversity of its sources, there is a need to produce an accurate text summary with the least time and effort. This summary must  preserve key information content and overall meaning of the original text. Text summarization is one of the most important applications of Natural Language Processing (NLP). The goal of automatic text summarization is to create summaries that are similar to human-created ones. However, in many cases, the readability of created summaries is not satisfactory,   because the summaries do not consider the meaning of the words and do not cover all the semantically relevant aspects of data. In this paper we use syntactic and semantic analysis to propose an automatic system of Arabic texts summarization. This system is capable of understanding the meaning of information and retrieves only the relevant part. The effectiveness and evaluation of the proposed work are demonstrated under EASC corpus using Rouge measure. The generated summaries will be compared against those done by human and precedent researches.

     

     

  • References

    1. [1] I. F. Moawad and M. Aref, Semantic graph reduction approach for abstractive Text Summarization, in Computer Engineering & Systems (ICCES), 2012 Seventh International Conference on, 2012, pp. 132-138. https://doi.org/10.1109/ICCES.2012.6408498.

      [2] Hans Peter Luhn, The automatic creation of literature abstracts, IBM Journal of research and development 2,2(1958), 159–165. https://doi.org/10.1147/rd.22.0159.

      [3] H. Oufaida, O. Nouali, and P. Blache, Minimum redundancy and maximum relevance for single and multi-document Arabic text summarization, Journal of King Saud University-Computer and Information Sciences, vol. 26, no. 4, pp. 450–461, (2014). https://doi.org/10.1016/j.jksuci.2014.06.008.

      [4] S. S. Ismail, M. Aref, and I. F. Moawad, A model for generating Arabic text from semantic representation, in 2015 11th International Computer Engineering Conference (ICENCO). IEEE, pp. 117–122, (2015). https://doi.org/10.1109/ICENCO.2015.7416335.

      [5] Muneer A. Alwan, Hoda M. Onsi, A Proposed Textual Graph Based Model for Arabic Multi-document Summarization, International Journal of Advanced Computer Science and Applications, Vol. 7, No. 6, (2016). https://doi.org/10.14569/IJACSA.2016.070656.

      [6] Aqil M. Azmia, Suha Al-Thanyyan, A text summarizer for Arabic, SciVerse Science Direct Computer Speech and Language 26 260–273, (2012) https://doi.org/10.1016/j.csl.2012.01.002.

      [7] Sulaiman Nasrallah Al Breem, Automatic Arabic Text Summarization for Large Scale Multiple Documents Using Genetic Algorithm and MapReduce, October (2016).

      [8] Hamzah Noori Fejer and Nazlia Omar, Automatic Multi-Document Arabic Text Summarization Using Clustering and Keyphrase Extraction, Journal of Artificial Intelligence 8 (1): 1-9, (2015). https://doi.org/10.3923/jai.2015.1.9.

      [9] Ibrahim Imam, Alaa Hamouda, Hebat Allah Abdul Khalek. An Ontology based Summarization System for Arabic Documents (OSSAD), International Journal of Computer Applications (0975 – 8887) Vol 74– No.17, July (2013). https://doi.org/10.5120/12980-0237.

      [10] A. Qaroush, I. Abu Farha, W. Ghanem et al. An efficient single document Arabic text summarization using a combination of statistical and semantic features, Journal of King Saud University – Computer and Information Sciences, March (2019). https://doi.org/10.1016/j.jksuci.2019.03.010.

      [11] Mahmoud El-Haj. Multi-document Arabic Text Summarisation, Computer Science and Electronic Engineering Conference (CEEC), (2012). https://doi.org/10.1109/CEEC.2011.5995822.

      [12] Jaap Kamp, Visualizing WordNet structure, Researchgate, January (2002).

      [13] Bill Black, Piek Vossen and Adam Pease, Articulate Software Arabic WordNet and the Challenges of Arabic, (2014).

      [14] Khan, Atif, A review on abstractive summarization methods, J. Theor. Appl. Inform. Tech. 59, 64–72 (2014).

      [15] Mehran Sahami, Timothy D. Heilman. A web-base kernel function for measuring the similarity of short text snippets, Proceedings of the 15th International Conference on World Wide Web, Scotland, (2006). https://doi.org/10.1145/1135777.1135834.

      [16] P. Resnik. Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language, Journal of Artiï¬cial Intelligence Research, (1999). https://doi.org/10.1613/jair.514.

      [17] Manjula Shenoy.K, Dr.K.C.Shet, Dr. U.Dinesh Acharya. a new similarity measure for taxonomy based on edge counting, International Journal of Web & Semantic Technology, Vol.3, No.4, October (2012). https://doi.org/10.5121/ijwest.2012.3403.

      [18] Z.Wu and M. Palmer. Verb semantics and lexical selection, Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics, Las Cruces, New Mexico, (1994). https://doi.org/10.3115/981732.981751.

      [19] D. Mclean, Y. Li & Z.A. Bandar. An approach for measuring semantic similarity between words using multiple information sources, IEEE Transactions on Knowledge and Data Eng., Vol. 15, No. 4. (2003). https://doi.org/10.1109/TKDE.2003.1209005.

      [20] Lin C.Y. 2004, Rouge: A package for automatic evaluation of summaries, In: Text Summarization Branches Out, http://www.aclweb.org/anthology/W04-1013.

  • Downloads

  • How to Cite

    Alkhudari, A. (2020). Developing a new approach to summarize Arabic text automatically using syntactic and semantic analysis. International Journal of Engineering & Technology, 9(2), 342-349. https://doi.org/10.14419/ijet.v9i2.30324

    Received date: 2020-01-17

    Accepted date: 2020-03-14

    Published date: 2020-04-03