Automatic Short Text Summarization Techniques in Social Media Platforms

Fahd A. Ghanem, M. C. Padma and Ramez Alkhatib ()
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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
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

Future Internet, 2023, vol. 15, issue 9, 1-27

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.

Keywords: text summarization; social media; evaluation metrics; abstractive summarization; extractive summarization; machine learning (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2023
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