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Интеллектуальная Система Тематического Исследования НАукометрических данных |
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EXTRACTIVE RUSSIAN TEXT SUMMARIZATION AS GREEDY SENTENCE SEQUENCE CONTINUATION SEARCH WITH PROBABILITIES FROM PRETRAINED LANGUAGE MODELSстатья
- Авторы: Polyakova Irina, Pogoreltsev Semyon
- Сборник: Open Semantic Technologies for Intelligent Systems
- Том: 5
- Год издания: 2021
- Место издания: БГУИР Минск Минск
- Первая страница: 303
- Последняя страница: 308
- Аннотация: Pretrained language models based on Trans-former have defined new state-of-the-art result on a wide range of tasks being finetuned or used as embedders.Models with Multi-head Self-Attention mechanism have defined a new standart of quality on text summarization task in English, while Transformer based models potentialfor extractive Russian text summarization has been barely explored. We propose a new method for extractive Russian text summarization, reducing the task to the selection of themost probable sequence of sentences. The new method beats ROUGE-1 and ROUGE-L scores of other models such as SummaRuNNer, and mBART evaluated on Gazeta dataset and is more preferred in human evaluation poll.
- Добавил в систему: Полякова Ирина Николаевна
Прикрепленные файлы
| № | Имя | Описание | Имя файла | Размер | Добавлен |
|---|---|---|---|---|---|
| 1. | Полный текст | statya_Polyakova_Pogoreltsev.pdf | 324,8 КБ | 9 декабря 2021 [PolyakovaIN] |