Automatic Short Text Summarization Techniques in Social Media Platforms

Author:

Ghanem Fahd A.12ORCID,Padma M. C.1,Alkhatib Ramez3ORCID

Affiliation:

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

2. Department of Computer Science, College of Education-Zabid, Hodeidah University, Hodeidah P.O. Box 3114, Yemen

3. 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.

Publisher

MDPI AG

Subject

Computer Networks and Communications

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