An overview of current issues in automatic text summarization of natural language using artificial intelligence methods

Authors

DOI:

https://doi.org/10.15587/2706-5448.2024.309472

Keywords:

automatic abstracting, natural language processing, artificial intelligence, generative models, neural networks, deep learning

Abstract

The object of the research is the task of automatic abstracting of natural language texts. The importance of these tasks is determined by the existing problem of creating essays that would adequately reflect the content of the original text and highlight key information. This task requires the ability of models to deeply analyze the context of the text, which complicates the abstracting process.

Results are presented that demonstrate the effectiveness of using generative models based on neural networks, text semantic analysis methods, and deep learning for automatic creation of abstracts. The use of models showed a high level of adequacy and informativeness of abstracts. GPT (Generative Pre-trained Transformer) generates text that looks like it was written by a human, which makes it useful for automatic essay generation.

For example, the GPT model generates abbreviated summaries based on input text, while the BERT model is used for summarizing texts in many areas, including search engines and natural language processing. This allows for short but informative abstracts that retain the essential content of the original and provides the ability to produce high-quality abstracts that can be used for abstracting web pages, emails, social media, and other content. Compared to traditional abstracting methods, artificial intelligence provides such advantages as greater accuracy, informativeness and the ability to process large volumes of text more efficiently, which facilitates access to information and improves productivity in text processing.

Automatic abstracting of texts using artificial intelligence models allows to significantly reduce the time required for the analysis of large volumes of textual information. This is especially important in today's information environment, where the amount of available data is constantly growing. The use of these models promotes efficient use of resources and increases overall productivity in a variety of fields, including scientific research, education, business and media.

Author Biographies

Oleksii Kuznietsov, National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute»

PhD Student

Department of System Design

Gennadiy Kyselov, National Technical University of Ukraine «Igor Sikorsky Kyiv Polytechnic Institute»

PhD, Senior Researcher, Associate Professor

Department of System Design

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An overview of current issues in automatic text summarization of natural language using artificial intelligence methods

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Published

2024-07-31

How to Cite

Kuznietsov, O., & Kyselov, G. (2024). An overview of current issues in automatic text summarization of natural language using artificial intelligence methods. Technology Audit and Production Reserves, 4(2(78), 12–19. https://doi.org/10.15587/2706-5448.2024.309472

Issue

Vol. 4 No. 2(78) (2024): Information and control systems

Section

Information Technologies

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Copyright (c) 2024 Oleksii Kuznietsov, Gennadiy Kyselov

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