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    <titleInfo>
        <title>Data Augmentation for Radiology Report Simplification</title>
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    <name type="personal">
        <namePart type="given">Ziyu</namePart>
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    <name type="personal">
        <namePart type="given">Slobodan</namePart>
        <namePart type="family">Vucetic</namePart>
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            <title>Findings of the Association for Computational Linguistics: EACL 2023</title>
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        <name type="personal">
            <namePart type="given">Andreas</namePart>
            <namePart type="family">Vlachos</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Isabelle</namePart>
            <namePart type="family">Augenstein</namePart>
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    <abstract>This work considers the development of a text simplification model to help patients better understand their radiology reports. This paper proposes a data augmentation approach to address the data scarcity issue caused by the high cost of manual simplification. It prompts a large foundational pre-trained language model to generate simplifications of unlabeled radiology sentences. In addition, it uses paraphrasing of labeled radiology sentences. Experimental results show that the proposed data augmentation approach enables the training of a significantly more accurate simplification model than the baselines.</abstract>
    <identifier type="citekey">yang-etal-2023-data</identifier>
    <identifier type="doi">10.18653/v1/2023.findings-eacl.144</identifier>
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        <url>https://aclanthology.org/2023.findings-eacl.144</url>
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    <part>
        <date>2023-05</date>
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            <start>1922</start>
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%0 Conference Proceedings
%T Data Augmentation for Radiology Report Simplification
%A Yang, Ziyu
%A Cherian, Santhosh
%A Vucetic, Slobodan
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Findings of the Association for Computational Linguistics: EACL 2023
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F yang-etal-2023-data
%X This work considers the development of a text simplification model to help patients better understand their radiology reports. This paper proposes a data augmentation approach to address the data scarcity issue caused by the high cost of manual simplification. It prompts a large foundational pre-trained language model to generate simplifications of unlabeled radiology sentences. In addition, it uses paraphrasing of labeled radiology sentences. Experimental results show that the proposed data augmentation approach enables the training of a significantly more accurate simplification model than the baselines.
%R 10.18653/v1/2023.findings-eacl.144
%U https://aclanthology.org/2023.findings-eacl.144
%U https://doi.org/10.18653/v1/2023.findings-eacl.144
%P 1922-1932

Markdown (Informal)

[Data Augmentation for Radiology Report Simplification](https://aclanthology.org/2023.findings-eacl.144) (Yang et al., Findings 2023)

• Data Augmentation for Radiology Report Simplification (Yang et al., Findings 2023)

ACL

• Ziyu Yang, Santhosh Cherian, and Slobodan Vucetic. 2023. Data Augmentation for Radiology Report Simplification. In Findings of the Association for Computational Linguistics: EACL 2023, pages 1922–1932, Dubrovnik, Croatia. Association for Computational Linguistics.