@inproceedings{galeshchuk-2023-abstractive,
    title = "Abstractive Summarization for the {U}krainian Language: Multi-Task Learning with Hromadske.ua News Dataset",
    author = "Galeshchuk, Svitlana",
    editor = "Romanyshyn, Mariana",
    booktitle = "Proceedings of the Second Ukrainian Natural Language Processing Workshop (UNLP)",
    month = may,
    year = "2023",
    address = "Dubrovnik, Croatia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2023.unlp-1.6/",
    doi = "10.18653/v1/2023.unlp-1.6",
    pages = "49--53",
    abstract = "Despite recent NLP developments, abstractive summarization remains a challenging task, especially in the case of low-resource languages like Ukrainian. The paper aims at improving the quality of summaries produced by mT5 for news in Ukrainian by fine-tuning the model with a mixture of summarization and text similarity tasks using summary-article and title-article training pairs, respectively. The proposed training set-up with small, base, and large mT5 models produce higher quality r{\'e}sum{\'e}. Besides, we present a new Ukrainian dataset for the abstractive summarization task that consists of circa 36.5K articles collected from Hromadske.ua until June 2021."
}