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Abstractive Summarization of Russian News Learning on Quality Mediaстатья
- Авторы: Чернышев Даниил Иванович, Добров Борис Викторович
- Сборник: Lecture Notes in Computer Science
- Том: 12602
- Год издания: 2021
- Издательство: Springer International Publishing AG
- Местоположение издательства: Cham, Switzerland
- Первая страница: 96
- Последняя страница: 104
- DOI: 10.1007/978-3-030-72610-2_7
- Аннотация: Summarization is becoming a demanded task in the modern world of ever-increasing document flow. This task allows to compress existing text while maintaining all salient information. However, building a neural summarization model requires training data which is scarce in some languages. In this work, we consider the problem of abstract summarization of news texts in Russian. We propose a new method for obtaining training data that uses the news leads of high-quality media that publishes news in accordance with the classical model. We prove dataset eligibility for training by building an abstractive summarization framework based on pre-trained language models and comparing summarization results with extractive baselines.
- Добавил в систему: Добров Борис Викторович