Efficiency of Neural Network Algorithms in Automatic Abstracting and Summarization Text

The article is devoted to the analysis of the role and efficiency of neural network algorithms in the tasks of automatic abstracting and summarization of texts, which are key in the field of natural language processing (NLP). The main goal of automatic abstracting is to extract and generate essential information from texts to provide quick access to the main content without having to read the whole document. The paper discusses the main challenges faced by developers in implementing abstracting algorithms, including understanding context, irony, maintaining text cohesion, and adapting to different languages and styles. Special attention is given to neural network models such as Transformer, BERT, and GPT, which have shown outstanding performance in automatic text abstracting due to their ability to learn on large amounts of data. The article also highlights the contributions of leading researchers in the field of deep learning and analyzes the methods underlying state-of-the-art NLP algorithms, highlighting the importance of continuous technological progress in improving abstracting quality and information accessibility. The article will be of interest to a wide range of readers, including researchers in the field of artificial intelligence and NLP, software developers engaged in automation of text processing, as well as specialists in areas where fast processing and analysis of large amounts of textual information is required, such as legal practice, medical diagnostics and scientific research. In addition, the material of the article will be useful for teachers and students studying data processing and artificial intelligence technologies, providing them with actual examples of applying theoretical knowledge in practical projects.