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        <title>Exploring Multitask Learning for Low-Resource Abstractive Summarization</title>
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    <name type="personal">
        <namePart type="given">Ahmed</namePart>
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    <name type="personal">
        <namePart type="given">Diane</namePart>
        <namePart type="family">Litman</namePart>
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    <name type="personal">
        <namePart type="given">Mohamed</namePart>
        <namePart type="family">Elaraby</namePart>
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            <title>Findings of the Association for Computational Linguistics: EMNLP 2021</title>
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        <name type="personal">
            <namePart type="given">Marie-Francine</namePart>
            <namePart type="family">Moens</namePart>
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        <name type="personal">
            <namePart type="given">Xuanjing</namePart>
            <namePart type="family">Huang</namePart>
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        <name type="personal">
            <namePart type="given">Lucia</namePart>
            <namePart type="family">Specia</namePart>
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            <namePart type="family">Yih</namePart>
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    <abstract>This paper explores the effect of using multitask learning for abstractive summarization in the context of small training corpora. In particular, we incorporate four different tasks (extractive summarization, language modeling, concept detection, and paraphrase detection) both individually and in combination, with the goal of enhancing the target task of abstractive summarization via multitask learning. We show that for many task combinations, a model trained in a multitask setting outperforms a model trained only for abstractive summarization, with no additional summarization data introduced. Additionally, we do a comprehensive search and find that certain tasks (e.g. paraphrase detection) consistently benefit abstractive summarization, not only when combined with other tasks but also when using different architectures and training corpora.</abstract>
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    <identifier type="doi">10.18653/v1/2021.findings-emnlp.142</identifier>
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        <date>2021-11</date>
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%0 Conference Proceedings
%T Exploring Multitask Learning for Low-Resource Abstractive Summarization
%A Magooda, Ahmed
%A Litman, Diane
%A Elaraby, Mohamed
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F magooda-etal-2021-exploring-multitask
%X This paper explores the effect of using multitask learning for abstractive summarization in the context of small training corpora. In particular, we incorporate four different tasks (extractive summarization, language modeling, concept detection, and paraphrase detection) both individually and in combination, with the goal of enhancing the target task of abstractive summarization via multitask learning. We show that for many task combinations, a model trained in a multitask setting outperforms a model trained only for abstractive summarization, with no additional summarization data introduced. Additionally, we do a comprehensive search and find that certain tasks (e.g. paraphrase detection) consistently benefit abstractive summarization, not only when combined with other tasks but also when using different architectures and training corpora.
%R 10.18653/v1/2021.findings-emnlp.142
%U https://aclanthology.org/2021.findings-emnlp.142
%U https://doi.org/10.18653/v1/2021.findings-emnlp.142
%P 1652-1661

Markdown (Informal)

[Exploring Multitask Learning for Low-Resource Abstractive Summarization](https://aclanthology.org/2021.findings-emnlp.142) (Magooda et al., Findings 2021)

• Exploring Multitask Learning for Low-Resource Abstractive Summarization (Magooda et al., Findings 2021)

ACL

• Ahmed Magooda, Diane Litman, and Mohamed Elaraby. 2021. Exploring Multitask Learning for Low-Resource Abstractive Summarization. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 1652–1661, Punta Cana, Dominican Republic. Association for Computational Linguistics.