<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="gurcke-etal-2021-assessing">
    <titleInfo>
        <title>Assessing the Sufficiency of Arguments through Conclusion Generation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Timon</namePart>
        <namePart type="family">Gurcke</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Milad</namePart>
        <namePart type="family">Alshomary</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Henning</namePart>
        <namePart type="family">Wachsmuth</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2021-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 8th Workshop on Argument Mining</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Khalid</namePart>
            <namePart type="family">Al-Khatib</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Yufang</namePart>
            <namePart type="family">Hou</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Manfred</namePart>
            <namePart type="family">Stede</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Punta Cana, Dominican Republic</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>The premises of an argument give evidence or other reasons to support a conclusion. However, the amount of support required depends on the generality of a conclusion, the nature of the individual premises, and similar. An argument whose premises make its conclusion rationally worthy to be drawn is called sufficient in argument quality research. Previous work tackled sufficiency assessment as a standard text classification problem, not modeling the inherent relation of premises and conclusion. In this paper, we hypothesize that the conclusion of a sufficient argument can be generated from its premises. To study this hypothesis, we explore the potential of assessing sufficiency based on the output of large-scale pre-trained language models. Our best model variant achieves an F1-score of .885, outperforming the previous state-of-the-art and being on par with human experts. While manual evaluation reveals the quality of the generated conclusions, their impact remains low ultimately.</abstract>
    <identifier type="citekey">gurcke-etal-2021-assessing</identifier>
    <identifier type="doi">10.18653/v1/2021.argmining-1.7</identifier>
    <location>
        <url>https://aclanthology.org/2021.argmining-1.7/</url>
    </location>
    <part>
        <date>2021-11</date>
        <extent unit="page">
            <start>67</start>
            <end>77</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Assessing the Sufficiency of Arguments through Conclusion Generation
%A Gurcke, Timon
%A Alshomary, Milad
%A Wachsmuth, Henning
%Y Al-Khatib, Khalid
%Y Hou, Yufang
%Y Stede, Manfred
%S Proceedings of the 8th Workshop on Argument Mining
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F gurcke-etal-2021-assessing
%X The premises of an argument give evidence or other reasons to support a conclusion. However, the amount of support required depends on the generality of a conclusion, the nature of the individual premises, and similar. An argument whose premises make its conclusion rationally worthy to be drawn is called sufficient in argument quality research. Previous work tackled sufficiency assessment as a standard text classification problem, not modeling the inherent relation of premises and conclusion. In this paper, we hypothesize that the conclusion of a sufficient argument can be generated from its premises. To study this hypothesis, we explore the potential of assessing sufficiency based on the output of large-scale pre-trained language models. Our best model variant achieves an F1-score of .885, outperforming the previous state-of-the-art and being on par with human experts. While manual evaluation reveals the quality of the generated conclusions, their impact remains low ultimately.
%R 10.18653/v1/2021.argmining-1.7
%U https://aclanthology.org/2021.argmining-1.7/
%U https://doi.org/10.18653/v1/2021.argmining-1.7
%P 67-77

Markdown (Informal)

[Assessing the Sufficiency of Arguments through Conclusion Generation](https://aclanthology.org/2021.argmining-1.7/) (Gurcke et al., ArgMining 2021)

• Assessing the Sufficiency of Arguments through Conclusion Generation (Gurcke et al., ArgMining 2021)

ACL

• Timon Gurcke, Milad Alshomary, and Henning Wachsmuth. 2021. Assessing the Sufficiency of Arguments through Conclusion Generation. In Proceedings of the 8th Workshop on Argument Mining, pages 67–77, Punta Cana, Dominican Republic. Association for Computational Linguistics.