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

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