<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="phan-etal-2021-matching">
    <titleInfo>
        <title>Matching The Statements: A Simple and Accurate Model for Key Point Analysis</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Hoang</namePart>
        <namePart type="family">Phan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Long</namePart>
        <namePart type="family">Nguyen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Khanh</namePart>
        <namePart type="family">Doan</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>Key Point Analysis (KPA) is one of the most essential tasks in building an Opinion Summarization system, which is capable of generating key points for a collection of arguments toward a particular topic. Furthermore, KPA allows quantifying the coverage of each summary by counting its matched arguments. With the aim of creating high-quality summaries, it is necessary to have an in-depth understanding of each individual argument as well as its universal semantic in a specified context. In this paper, we introduce a promising model, named Matching the Statements (MTS) that incorporates the discussed topic information into arguments/key points comprehension to fully understand their meanings, thus accurately performing ranking and retrieving best-match key points for an input argument. Our approach has achieved the 4th place in Track 1 of the Quantitative Summarization – Key Point Analysis Shared Task by IBM, yielding a competitive performance of 0.8956 (3rd) and 0.9632 (7th) strict and relaxed mean Average Precision, respectively.</abstract>
    <identifier type="citekey">phan-etal-2021-matching</identifier>
    <identifier type="doi">10.18653/v1/2021.argmining-1.17</identifier>
    <location>
        <url>https://aclanthology.org/2021.argmining-1.17</url>
    </location>
    <part>
        <date>2021-11</date>
        <extent unit="page">
            <start>165</start>
            <end>174</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Matching The Statements: A Simple and Accurate Model for Key Point Analysis
%A Phan, Hoang
%A Nguyen, Long
%A Doan, Khanh
%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 phan-etal-2021-matching
%X Key Point Analysis (KPA) is one of the most essential tasks in building an Opinion Summarization system, which is capable of generating key points for a collection of arguments toward a particular topic. Furthermore, KPA allows quantifying the coverage of each summary by counting its matched arguments. With the aim of creating high-quality summaries, it is necessary to have an in-depth understanding of each individual argument as well as its universal semantic in a specified context. In this paper, we introduce a promising model, named Matching the Statements (MTS) that incorporates the discussed topic information into arguments/key points comprehension to fully understand their meanings, thus accurately performing ranking and retrieving best-match key points for an input argument. Our approach has achieved the 4th place in Track 1 of the Quantitative Summarization – Key Point Analysis Shared Task by IBM, yielding a competitive performance of 0.8956 (3rd) and 0.9632 (7th) strict and relaxed mean Average Precision, respectively.
%R 10.18653/v1/2021.argmining-1.17
%U https://aclanthology.org/2021.argmining-1.17
%U https://doi.org/10.18653/v1/2021.argmining-1.17
%P 165-174

Markdown (Informal)

[Matching The Statements: A Simple and Accurate Model for Key Point Analysis](https://aclanthology.org/2021.argmining-1.17) (Phan et al., ArgMining 2021)

• Matching The Statements: A Simple and Accurate Model for Key Point Analysis (Phan et al., ArgMining 2021)

ACL

• Hoang Phan, Long Nguyen, Long Nguyen, and Khanh Doan. 2021. Matching The Statements: A Simple and Accurate Model for Key Point Analysis. In Proceedings of the 8th Workshop on Argument Mining, pages 165–174, Punta Cana, Dominican Republic. Association for Computational Linguistics.