Author/Authors :
Abubakar Bichi, Abdulkadir School of Computing - University Technology Malaysia, Johor, Malaysia , Samsudin, Ruhaidah School of Computing - University Technology Malaysia, Johor, Malaysia , Rohayanti, Hassa School of Computing - University Technology Malaysia, Johor, Malaysia , Almekhlafi, Khalil Taibah University, CBA-Yanbu, Saudi Arabia
Abstract :
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.
