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Automatic Short Text Summarization Techniques in Social Media Platforms

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  • Fahd A. Ghanem

    (Department of Computer Science & Engineering, PES College of Engineering, (Affiliated to University of Mysore), Mandya 571401, Karnataka, India
    Department of Computer Science, College of Education-Zabid, Hodeidah University, Hodeidah P.O. Box 3114, Yemen)

  • M. C. Padma

    (Department of Computer Science & Engineering, PES College of Engineering, (Affiliated to University of Mysore), Mandya 571401, Karnataka, India)

  • Ramez Alkhatib

    (BMB Nord, Research Center Borstel, Parkallee 35, 23845 Borstel, Germany)

Abstract

The rapid expansion of social media platforms has resulted in an unprecedented surge of short text content being generated on a daily basis. Extracting valuable insights and patterns from this vast volume of textual data necessitates specialized techniques that can effectively condense information while preserving its core essence. In response to this challenge, automatic short text summarization (ASTS) techniques have emerged as a compelling solution, gaining significant importance in their development. This paper delves into the domain of summarizing short text on social media, exploring various types of short text and the associated challenges they present. It also investigates the approaches employed to generate concise and meaningful summaries. By providing a survey of the latest methods and potential avenues for future research, this paper contributes to the advancement of ASTS in the ever-evolving landscape of social media communication.

Suggested Citation

  • Fahd A. Ghanem & M. C. Padma & Ramez Alkhatib, 2023. "Automatic Short Text Summarization Techniques in Social Media Platforms," Future Internet, MDPI, vol. 15(9), pages 1-27, September.
    • Handle: RePEc:gam:jftint:v:15:y:2023:i:9:p:311-:d:1239212
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    References listed on IDEAS

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    1. Arkaitz Zubiaga, 2018. "A longitudinal assessment of the persistence of twitter datasets," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 69(8), pages 974-984, August.
    2. Koustav Rudra & Ashish Sharma & Niloy Ganguly & Muhammad Imran, 2018. "Classifying and Summarizing Information from Microblogs During Epidemics," Information Systems Frontiers, Springer, vol. 20(5), pages 933-948, October.
    3. Nirmalya Thakur, 2022. "A Large-Scale Dataset of Twitter Chatter about Online Learning during the Current COVID-19 Omicron Wave," Data, MDPI, vol. 7(8), pages 1-16, August.
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