COMPREHENSIVE REVIEW OF AUTOMATIC TEXT SUMMARIZATION TECHNIQUES

被引：0

作者：

Cajueiro, Daniel O. ^{[1
,2
,3
]}

Nery, Arthur G. ^{[1
,4
]}

Tavares, Igor ^{[4
]}

De Melo, Maísa K. ^{[3
,5
]}

dos Reis, Silvia A. ^{[6
]}

Weigang, Li ^{[7
]}

Celestino, Victor R.R. ^{[3
,6
]}

机构：

[1] Department of Economics, Universidade de Brasília (UnB), Brazil

[2] Nacional Institute of Science and Technology for Complex Systems (INCT-SC), Universidade de Brasília (UnB), Brazil

[3] Machine Learning Laboratory in Finance and Organizations (LAMFO), Universidade de Brasília (UnB), Brasília, Brazil

[4] Mechanic Engineering Department, Universidade de Brasília (UnB), Brazil

[5] Department of Mathematics, Instituto Federal de Minas Gerais, Brazil

[6] Business Department, Universidade de Brasília (UnB), Brazil

[7] Computer Science Department, Universidade de Brasília (UnB), Brazil

来源：

Computing and Informatics | 2024年 / 43卷 / 05期

关键词：

Adversarial machine learning - Contrastive Learning;

D O I：

10.31577/cai202451185

中图分类号：

学科分类号：

摘要：

Automatic Text Summarization (ATS) is a fundamental aspect of Natural Language Processing (NLP) that allows for the conversion of lengthy text documents into concise summaries that retain the essential information based on specific criteria. In this paper, we present a literature review on the topic of ATS, which includes an overview of the various approaches to ATS, categorized by the mechanisms they use to generate a summary. By organizing these approaches based on their underlying mechanisms, we provide a comprehensive understanding of the current state-of-the-art in ATS systems. © 2024 Slovak Academy of Sciences. All rights reserved.

引用

页码：1185 / 1218

共 50 条

[41] Readability Evaluation Metrics for Indonesian Automatic Text Summarization: A Systematic Review
Maylawati, Dian Sa'adillah
Kumar, Yogan Jaya
Kasmin, Fauziah Binti
Ramdhani, Muhammad Ali
Journal of Engineering Science and Technology Review, 2024, 17 (05) : 199 - 210
[42] State-of-the-art approach to extractive text summarization: a comprehensive review
Avaneesh Kumar Yadav
Rama Shankar Ranvijay
Ashish Kumar Yadav
Multimedia Tools and Applications, 2023, 82 : 29135 - 29197
[43] State-of-the-art approach to extractive text summarization: a comprehensive review
Yadav, Avaneesh Kumar
Ranvijay
Yadav, Rama Shankar
Maurya, Ashish Kumar
MULTIMEDIA TOOLS AND APPLICATIONS, 2023, 82 (19) : 29135 - 29197
[44] Practical approach to automatic text summarization
Hynek, J
Jezek, K
FROM INFORMATION TO KNOWLEDGE, 2003, : 378 - 388
[45] Sentence reduction for automatic text summarization
Jing, HY
6TH APPLIED NATURAL LANGUAGE PROCESSING CONFERENCE/1ST MEETING OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS, PROCEEDINGS OF THE CONFERENCE AND PROCEEDINGS OF THE ANLP-NAACL 2000 STUDENT RESEARCH WORKSHOP, 2000, : 310 - 315
[46] AUTOMATIC TEXT SUMMARIZATION FOR INDIAN LANGUAGES
Kumar, Jeetendra
Shekhar, Shashi
Gupta, Rashmi
EVERYMANS SCIENCE, 2022, 57 (01):
[47] Intelligent model for automatic text summarization
Binwahlan, M.S.
Salim, N.
Suanmali, L.
Information Technology Journal, 2009, 8 (08) : 1249 - 1255
[48] Psychological Features for Automatic Text Summarization
Losada, David E.
Parapar, Javier
INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS, 2017, 25 : 129 - 149
[49] Statistical Models to Automatic Text Summarization
Pham Trong Nguyen
Co Ton Minh Dang
FUTURE DATA AND SECURITY ENGINEERING, FDSE 2018, 2018, 11251 : 486 - 498
[50] Advances in automatic text summarization.
Saggion, H
KNOWLEDGE ORGANIZATION, 2000, 27 (03): : 178 - 180

← 1 2 3 4 5 →