Математическая модель процесса автоматизированного построения обзорных текстов, основанного на использовании цитатно-осведомленной суммаризации

В работе рассматривается задача автоматизированного построения научных обзорных текстов на основе анализа корпуса научных публикаций. Разработана математическая модель, описывающая подход, основанный на использовании цитатно-осведомленной суммаризации с предварительным отбором цитатных фрагментов, и формализующая полный цикл обработки данных от отбора публикаций и извлечения цитирующих фрагментов до генерации частных саммари и итогового обзорного текста. Модель задает единое формальное описание последовательности операторов преобразования данных и включает систему критериев качества, обеспечивающих контроль правдоподобности, покрытия и фактологической согласованности результатов на всех этапах обработки. На основе разработанной модели реализована программная система в виде модульного конвейера. С использованием разработанной модели экспериментальные исследования на датасете SurGE, включающем 114 тематик, более 7 тыс. цитируемых и свыше 73 тыс. цитирующих публикаций. Показано, что использование цитатно-осведомленного подхода с предварительным отбором фрагментов обеспечивает улучшение качества генерации саммари по сравнению с альтернативными методами. Для итоговых обзорных текстов достигнуты следующие значения критериев качества: правдоподобность – 0,8744, покрытие – 0,9356, фактологическая достоверность – 0,9713 и LLM-оценка – 0,9232, что превосходит результаты генерации на основе полного текста источников (на 7,67 % для правдоподобности, 4,63 % для покрытия, 9,22 % для фактологической согласованности и 4,42 % для LLM-оценки). Полученные результаты подтверждают эффективность предложенного подхода и разработанной модели, и их применимость для задач автоматизированного формирования достоверных научных обзоров.

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