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        <title>Abstractive Summarization for the Ukrainian Language: Multi-Task Learning with Hromadske.ua News Dataset</title>
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    <name type="personal">
        <namePart type="given">Svitlana</namePart>
        <namePart type="family">Galeshchuk</namePart>
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            <title>Proceedings of the Second Ukrainian Natural Language Processing Workshop (UNLP)</title>
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        <name type="personal">
            <namePart type="given">Mariana</namePart>
            <namePart type="family">Romanyshyn</namePart>
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    <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 résumé. 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.</abstract>
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    <identifier type="doi">10.18653/v1/2023.unlp-1.6</identifier>
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        <date>2023-05</date>
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            <start>49</start>
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%0 Conference Proceedings
%T Abstractive Summarization for the Ukrainian Language: Multi-Task Learning with Hromadske.ua News Dataset
%A Galeshchuk, Svitlana
%Y Romanyshyn, Mariana
%S Proceedings of the Second Ukrainian Natural Language Processing Workshop (UNLP)
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F galeshchuk-2023-abstractive
%X 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 résumé. 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.
%R 10.18653/v1/2023.unlp-1.6
%U https://aclanthology.org/2023.unlp-1.6
%U https://doi.org/10.18653/v1/2023.unlp-1.6
%P 49-53

Markdown (Informal)

[Abstractive Summarization for the Ukrainian Language: Multi-Task Learning with Hromadske.ua News Dataset](https://aclanthology.org/2023.unlp-1.6) (Galeshchuk, UNLP 2023)

• Abstractive Summarization for the Ukrainian Language: Multi-Task Learning with Hromadske.ua News Dataset (Galeshchuk, UNLP 2023)

ACL

• Svitlana Galeshchuk. 2023. Abstractive Summarization for the Ukrainian Language: Multi-Task Learning with Hromadske.ua News Dataset. In Proceedings of the Second Ukrainian Natural Language Processing Workshop (UNLP), pages 49–53, Dubrovnik, Croatia. Association for Computational Linguistics.