Graph-Based Extractive Text Summarization Models: A Systematic Review | ||
| Journal of Information Technology Management | ||
| دوره 14، Special Issue: 5th International Conference of Reliable Information and Communication Technology (IRICT 2020)، 2022، صفحه 184-202 اصل مقاله (710.03 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/jitm.2022.84899 | ||
| نویسندگان | ||
| Abdulkadir Abubakar Bichi* 1؛ Pantea Keikhosrokiani2؛ Rohayanti Hassan3؛ Khalil Almekhlafi4 | ||
| 1School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Johor-Malaysia | ||
| 2School of Computer Sciences, University Sains Malaysia, 11800 Minden, Penang, Malaysia. | ||
| 3Senior Lecturer, School of Computing, University Technology Malaysia, 81310 Johor Bahru, Johor, Malaysia | ||
| 4Assistant Professor, Taibah University, CBA-Yanbu, 42353, Saudi Arabia. | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| Natural Languages Processing؛ Text Mining؛ Graph approaches | ||
| مراجع | ||
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