COMPREHENSIVE REVIEW OF AUTOMATIC TEXT SUMMARIZATION TECHNIQUES

被引：0

作者：

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

Nery, Arthur G. ^{[1
,3
]}

Tavares, Igor ^{[4
]}

De Melo, Maisa K. ^{[3
,5
]}

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

Weigang, Li ^{[7
]}

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

机构：

[1] Univ Brasilia UnB, Dept Econ, Brasilia, Brazil

[2] Univ Brasilia UnB, Nacl Inst Sci & Technol Complex Syst INCT SC, Brasilia, Brazil

[3] Univ Brasilia UnB, Machine Learning Lab Finance & Org LAMFO, Brasilia, Brazil

[4] Univ Brasilia UnB, Mech Engn Dept, Brasilia, Brazil

[5] Inst Fed Minas Gerais, Dept Math, Belo Horizonte, Brazil

[6] Univ Brasilia UnB, Business Dept, Brasilia, Brazil

[7] Univ Brasilia UnB, Comp Sci Dept, Brasilia, Brazil

来源：

COMPUTING AND INFORMATICS | 2024年 / 43卷 / 05期

关键词：

Machine learning; natural language processing; summarization; ABSTRACTS;

D O I：

10.31577/cai2024_5_1185

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

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.

引用

页码：1185 / 1218

页数：34