<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zhou-etal-2023-survey">
    <titleInfo>
        <title>A Survey of Evaluation Methods of Generated Medical Textual Reports</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Yongxin</namePart>
        <namePart type="family">Zhou</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Fabien</namePart>
        <namePart type="family">Ringeval</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">François</namePart>
        <namePart type="family">Portet</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2023-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 5th Clinical Natural Language Processing Workshop</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Tristan</namePart>
            <namePart type="family">Naumann</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Asma</namePart>
            <namePart type="family">Ben Abacha</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Steven</namePart>
            <namePart type="family">Bethard</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Kirk</namePart>
            <namePart type="family">Roberts</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Anna</namePart>
            <namePart type="family">Rumshisky</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Toronto, Canada</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Medical Report Generation (MRG) is a sub-task of Natural Language Generation (NLG) and aims to present information from various sources in textual form and synthesize salient information, with the goal of reducing the time spent by domain experts in writing medical reports and providing support information for decision-making. Given the specificity of the medical domain, the evaluation of automatically generated medical reports is of paramount importance to the validity of these systems. Therefore, in this paper, we focus on the evaluation of automatically generated medical reports from the perspective of automatic and human evaluation. We present evaluation methods for general NLG evaluation and how they have been applied to domain-specific medical tasks. The study shows that MRG evaluation methods are very diverse, and that further work is needed to build shared evaluation methods. The state of the art also emphasizes that such an evaluation must be task specific and include human assessments, requesting the participation of experts in the field.</abstract>
    <identifier type="citekey">zhou-etal-2023-survey</identifier>
    <identifier type="doi">10.18653/v1/2023.clinicalnlp-1.48</identifier>
    <location>
        <url>https://aclanthology.org/2023.clinicalnlp-1.48</url>
    </location>
    <part>
        <date>2023-07</date>
        <extent unit="page">
            <start>447</start>
            <end>459</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T A Survey of Evaluation Methods of Generated Medical Textual Reports
%A Zhou, Yongxin
%A Ringeval, Fabien
%A Portet, François
%Y Naumann, Tristan
%Y Ben Abacha, Asma
%Y Bethard, Steven
%Y Roberts, Kirk
%Y Rumshisky, Anna
%S Proceedings of the 5th Clinical Natural Language Processing Workshop
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F zhou-etal-2023-survey
%X Medical Report Generation (MRG) is a sub-task of Natural Language Generation (NLG) and aims to present information from various sources in textual form and synthesize salient information, with the goal of reducing the time spent by domain experts in writing medical reports and providing support information for decision-making. Given the specificity of the medical domain, the evaluation of automatically generated medical reports is of paramount importance to the validity of these systems. Therefore, in this paper, we focus on the evaluation of automatically generated medical reports from the perspective of automatic and human evaluation. We present evaluation methods for general NLG evaluation and how they have been applied to domain-specific medical tasks. The study shows that MRG evaluation methods are very diverse, and that further work is needed to build shared evaluation methods. The state of the art also emphasizes that such an evaluation must be task specific and include human assessments, requesting the participation of experts in the field.
%R 10.18653/v1/2023.clinicalnlp-1.48
%U https://aclanthology.org/2023.clinicalnlp-1.48
%U https://doi.org/10.18653/v1/2023.clinicalnlp-1.48
%P 447-459

Markdown (Informal)

[A Survey of Evaluation Methods of Generated Medical Textual Reports](https://aclanthology.org/2023.clinicalnlp-1.48) (Zhou et al., ClinicalNLP 2023)

• A Survey of Evaluation Methods of Generated Medical Textual Reports (Zhou et al., ClinicalNLP 2023)

ACL

• Yongxin Zhou, Fabien Ringeval, and François Portet. 2023. A Survey of Evaluation Methods of Generated Medical Textual Reports. In Proceedings of the 5th Clinical Natural Language Processing Workshop, pages 447–459, Toronto, Canada. Association for Computational Linguistics.