Open Access

ARTICLE

TG-SMR: A Text Summarization Algorithm Based on Topic and Graph Models

Mohamed Ali Rakrouki^1,*, Nawaf Alharbe¹, Mashael Khayyat², Abeer Aljohani¹

1 Applied College, Taibah University, Medina, 42353, Saudi Arabia
2 College of Computer Science and Engineering, University of Jeddah, Jeddah, 21959, Saudi Arabia

* Corresponding Author: Mohamed Ali Rakrouki. Email:

Computer Systems Science and Engineering 2023, 45(1), 395-408. https://doi.org/10.32604/csse.2023.029032

Received 23 February 2022; Accepted 15 April 2022; Issue published 16 August 2022

Abstract

Recently, automation is considered vital in most fields since computing methods have a significant role in facilitating work such as automatic text summarization. However, most of the computing methods that are used in real systems are based on graph models, which are characterized by their simplicity and stability. Thus, this paper proposes an improved extractive text summarization algorithm based on both topic and graph models. The methodology of this work consists of two stages. First, the well-known TextRank algorithm is analyzed and its shortcomings are investigated. Then, an improved method is proposed with a new computational model of sentence weights. The experimental results were carried out on standard DUC2004 and DUC2006 datasets and compared to four text summarization methods. Finally, through experiments on the DUC2004 and DUC2006 datasets, our proposed improved graph model algorithm TG-SMR (Topic Graph-Summarizer) is compared to other text summarization systems. The experimental results prove that the proposed TG-SMR algorithm achieves higher ROUGE scores. It is foreseen that the TG-SMR algorithm will open a new horizon that concerns the performance of ROUGE evaluation indicators.

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