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Daniel O. Cajueiro
Department of Economics, Universidade de Brasília (UnB), Brazil & Nacional Institute of Science and Technology for Complex Systems (INCT-SC) Universidade de Brasília (UnB), Brazil & Machine Learning Laboratory in Finance and Organizations (LAMFO) Universidade de Brasília (UnB), Brasília, Brazil
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Arthur G. Nery
Department of Economics, Universidade de Brasília (UnB), Brazil & Machine Learning Laboratory in Finance and Organizations (LAMFO) Universidade de Brasília (UnB), Brasília, Brazil
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Igor Tavares
Mechanic Engineering Department, Universidade de Brasília (UnB), Brazil
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Maísa K. De Melo
Department of Mathematics, Instituto Federal de Minas Gerais, Brazil & Machine Learning Laboratory in Finance and Organizations (LAMFO) Universidade de Brasília (UnB), Brasília, Brazil
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Silvia A. dos Reis
Business Department, Universidade de Brasília (UnB), Brazil
Comprehensive Review of Automatic Text Summarization Techniques
keywords: Machine learning, natural language processing, summarization
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.
mathematics subject classification 2000: 68Txx, 68Uxx
reference: Vol. 43, 2024, No. 5, pp. 1185–1218
