Virtual Communication and Social Networks Virtual Communication and Social Networks Виртуальная коммуникация и социальные сети 2782-4799 2782-4802 89112 10.21603/2782-4799-2024-3-3-203-222 ZDVTUU Коммуникативистика и когнитивные науки Communication Studies and Cognitive Sciences Коммуникативистика и когнитивные науки Intelligent Text Processing: A Review of Automated Summarization Methods Интеллектуальная обработка текстовой информации: обзор автоматизированных методов суммаризации Сорокина Светлана Геннадьевна Sorokina Svetlana Gennad'evna lana40ina@mail.ru Первый Московский государственный медицинский университет им. И. М. Сеченова Минздрава России Москва Россия Sechenov First Moscow State Medical University Moscow Russian Federation 01 10 2024 01 10 2024 3 3 203 222 03 06 2024 09 09 2024 https://jsocnet.ru/en/nauka/article/89112/view

Интерес к инновационным технологическим стратегиям и современным цифровым инструментам обработки информации значительно возрос в связи с необходимостью управления большими массивами неструктурированных данных. Автоматизированная суммаризация – важный инструмент в различных областях, требующих эффективного анализа и обработки больших объемов текстовой информации. В статье представлен обзор актуальных парадигм и сервисов автоматизированной суммаризации на основе междисциплинарных исследований в области лингвистики, компьютерных технологий и искусственного интеллекта. Особое внимание уделено синтаксическим и лексическим приемам, используемым нейро­сетевыми моделями для сжатия текста. В качестве примера рассмотрены сервисы QuillBot, Summate.it, WordTune, SciSummary, Scholarcy и OpenAI ChatGPT. Выявлено, что современные модели автоматизированной суммаризации успешно применяют экстрактивные и абстрактивные методы для создания резюме разного качества и объема. Экстрактивный подход основан на выделении наиболее значимых предложений в исходном тексте. Абстрактивные алгоритмы создают новые формулировки, сохраняя основную мысль оригинального текста. Автоматизированные суммаризаторы эффективно используют приемы сжатия текста (устранение избыточной информации, упрощение сложных конструкций и обобщение данных), присущие человеку в процессе обработки текстовой информации. Эти технологии обеспечивают высокую точность и связность генерируемых резюме, хотя каждая модель имеет свои ограничения. Для достижения оптимальных результатов важно учитывать специфику задачи и выбирать подходящую модель суммаризации: экстрактивную – для краткости и точности; абстрактивную – для более глубокой смысловой обработки текстовых данных.

Interest in innovative technological strategies and modern digital tools has increased significantly due to the need to manage large amounts of unstructured data. This paper reviews current paradigms and services for automated summarization, developed based on interdisciplinary research in linguistics, computer technologies, and artificial intelligence. It focuses on syntactic and lexical techniques employed by neural network models for text compression. The paper presents performance examples of such AI-powered services as QuillBot, Summate.it, WordTune, SciSummary, Scholarcy, and OpenAI ChatGPT. The contemporary automated models proved effective in using extractive and abstractive methods to generate summaries of varying quality and length. The extractive approach relies on identifying the most significant sentences from the original text, while abstractive algorithms create new sentence structures that preserve the main idea of the original content. Automated summarizers effectively utilize text compression techniques that are inherent to human approach to text processing, e.g., they exclude redundant information, simplify complex structures, and generalize data. These technologies provide high accuracy and coherence in the generated summaries, though each summarization model has its limitations. Optimal results depend on the specifics of the task at hand: extractive models provide brevity and precision while abstractive ones allow for deeper semantic processing. Automated summarization is becoming an important tool in various fields that require effective analysis and processing of large text data.

 автоматизированная суммаризация авторезюмирование экстрактивная суммаризация абстрактивная суммаризация нейронные сети искусственный интеллект междисциплинарные исследования automated summarization auto summary extractive summarization abstractive summarization neural networks artificial intelligence interdisciplinary research

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