IDEAS home Printed from https://ideas.repec.org/a/bjc/journl/v10y2023i2p74-84.html
   My bibliography  Save this article

Polynomial Networks Model for Arabic Text Summarization

Author

Listed:
  • Mohammed Salem Binwahlan

    (Information Technology Department, College of Applied Science, Seiyun University)

Abstract

Online sources enable users to get their information needs. But, finding the relevant information, in such sources, became a big challenge and time consumption due to the massive size of data those sources contain. Automatic text summarization is an important facility to overcome such a problem. To this end, many text summarization algorithms have been proposed based on different techniques and different methodologies. Text features are the main entries in text summarization, where each feature plays a different role for showing the most important content. This study introduces the polynomial networks (PN) for Arabic text summarization problem. The role of the polynomial networks (PN) is to compute optimal weights, through the training process of PN classifier, where these weights were used to adjust the text features scores. Adjusting the text features scores creates a fair dealing with those features according to their importance and plays an important role in the differentiation between higher and less important ones. The proposed model produces a summary of an original document through classifying each sentence as summary sentence or non-summary sentence. Six summarizers (Naïve Bayes, AQBTSS, Gen–Summ, LSA–Summ, Sakhr1 and Baseline–1) were used as benchmarks. The proposed model and benchmarks were evaluated using the same dataset (EASC – the Essex Arabic Summaries Corpus). The results shew that the proposed model defeats the all six summarizers. In addition, the rate error results of both the proposed model (PN classifier) and Naïve Bayes (NB classifier), it is a clear that the proposed model (PN classifier) works better. In general, the proposed model provides a good enhancement indicating that the polynomial networks (PN) are a promising technique for text summarization problem.

Suggested Citation

  • Mohammed Salem Binwahlan, 2023. "Polynomial Networks Model for Arabic Text Summarization," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 10(2), pages 74-84, February.
    • Handle: RePEc:bjc:journl:v:10:y:2023:i:2:p:74-84
    as

    Download full text from publisher

    File URL: https://www.rsisinternational.org/journals/ijrsi/digital-library/volume-10-issue-2/74-84.pdf
    Download Restriction: no
    File URL: https://www.rsisinternational.org/virtual-library/papers/polynomial-networks-model-for-arabic-text-summarization/?utm_source=Netcore&utm_medium=Email&utm_content=26octkrish&utm_campaign=Krishuo1
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Irina Wedel & Michael Palk & Stefan Voß, 2022. "A Bilingual Comparison of Sentiment and Topics for a Product Event on Twitter," Information Systems Frontiers, Springer, vol. 24(5), pages 1635-1646, October.
    2. Mingxi Zhang & Pohan Li & Wei Wang, 2017. "An index-based algorithm for fast on-line query processing of latent semantic analysis," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-23, May.
    3. Curci, Ylenia & Mongeau Ospina, Christian A., 2016. "Investigating biofuels through network analysis," Energy Policy, Elsevier, vol. 97(C), pages 60-72.
    4. Jean-Charles Bricongne & Baptiste Meunier & Raquel Caldeira, 2024. "Should Central Banks Care About Text Mining? A Literature Review," Working papers 950, Banque de France.
    5. Chao Wei & Senlin Luo & Xincheng Ma & Hao Ren & Ji Zhang & Limin Pan, 2016. "Locally Embedding Autoencoders: A Semi-Supervised Manifold Learning Approach of Document Representation," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-20, January.
    6. Triss Ashton & Nicholas Evangelopoulos & Victor Prybutok, 2014. "Extending monitoring methods to textual data: a research agenda," Quality & Quantity: International Journal of Methodology, Springer, vol. 48(4), pages 2277-2294, July.
    7. Beaupain, Renaud & Girard, Alexandre, 2020. "The value of understanding central bank communication," Economic Modelling, Elsevier, vol. 85(C), pages 154-165.
    8. Hoppenbrouwers, J.J.A.C. & Paijmans, J.J., 2000. "Invading the fortress : How to beseige reinforced information bunkers," Other publications TiSEM 62e33d30-6377-48c8-9bdf-b, Tilburg University, School of Economics and Management.
    9. Whalen, Ryan, 2018. "Boundary spanning innovation and the patent system: Interdisciplinary challenges for a specialized examination system," Research Policy, Elsevier, vol. 47(7), pages 1334-1343.
    10. Romain Gauchon & Stéphane Loisel & Jean-Louis Rullière, 2020. "Health-policyholder clustering using health consumption," Post-Print hal-02156058, HAL.
    11. Yueyang Zhao & Lei Cui, 2023. "Fusion Matrix–Based Text Similarity Measures for Clustering of Retrieval Results," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(2), pages 1163-1186, February.
    12. Zekun Wang & Zhaohua Deng & Xiang Wu, 2019. "Status Quo of Professional–Patient Relations in the Internet Era: Bibliometric and Co-Word Analyses," IJERPH, MDPI, vol. 16(7), pages 1-19, April.
    13. Paramveer S. Dhillon & Sinan Aral, 2021. "Modeling Dynamic User Interests: A Neural Matrix Factorization Approach," Marketing Science, INFORMS, vol. 40(6), pages 1059-1080, November.
    14. Maksym Polyakov & Morteza Chalak & Md. Sayed Iftekhar & Ram Pandit & Sorada Tapsuwan & Fan Zhang & Chunbo Ma, 2018. "Authorship, Collaboration, Topics, and Research Gaps in Environmental and Resource Economics 1991–2015," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 71(1), pages 217-239, September.
    15. Ding, Ying, 2011. "Community detection: Topological vs. topical," Journal of Informetrics, Elsevier, vol. 5(4), pages 498-514.
    16. Klaus Gugler & Florian Szücs & Ulrich Wohak, 2023. "Start-up Acquisitions, Venture Capital and Innovation: A Comparative Study of Google, Apple, Facebook, Amazon and Microsoft," Department of Economics Working Papers wuwp340, Vienna University of Economics and Business, Department of Economics.
    17. Md Nazrul Islam & Md Mofazzal Hossain & Md Shafayet Shahed Ornob, 2024. "Business research on Industry 4.0: a systematic review using topic modelling approach," Future Business Journal, Springer, vol. 10(1), pages 1-15, December.
    18. Juan Shi & Kin Keung Lai & Ping Hu & Gang Chen, 2018. "Factors dominating individual information disseminating behavior on social networking sites," Information Technology and Management, Springer, vol. 19(2), pages 121-139, June.
    19. Meen Chul Kim & Yongjun Zhu & Chaomei Chen, 2016. "How are they different? A quantitative domain comparison of information visualization and data visualization (2000–2014)," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(1), pages 123-165, April.
    20. Ganesh Dash & Chetan Sharma & Shamneesh Sharma, 2023. "Sustainable Marketing and the Role of Social Media: An Experimental Study Using Natural Language Processing (NLP)," Sustainability, MDPI, vol. 15(6), pages 1-16, March.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bjc:journl:v:10:y:2023:i:2:p:74-84. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Dr. Renu Malsaria (email available below). General contact details of provider: https://rsisinternational.org/journals/ijrsi/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.