Graph-Based Extractive Text Summarization Models: A Systematic Review

Document Type : Research Paper


1 School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Johor-Malaysia

2 School of Computer Sciences, University Sains Malaysia, 11800 Minden, Penang, Malaysia.

3 Senior Lecturer, School of Computing, University Technology Malaysia, 81310 Johor Bahru, Johor, Malaysia

4 Assistant Professor, Taibah University, CBA-Yanbu, 42353, Saudi Arabia.


The volume of digital text data is continuously increasing both online and offline storage, which makes it difficult to read across documents on a particular topic and find the desired information within a possible available time. This necessitates the use of technique such as automatic text summarization. Many approaches and algorithms have been proposed for automatic text summarization including; supervised machine learning, clustering, graph-based and lexical chain, among others. This paper presents a novel systematic review of various graph-based automatic text summarization models.


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