<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="gao-etal-2020-supert">
    <titleInfo>
        <title>SUPERT: Towards New Frontiers in Unsupervised Evaluation Metrics for Multi-Document Summarization</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Yang</namePart>
        <namePart type="family">Gao</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Wei</namePart>
        <namePart type="family">Zhao</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Steffen</namePart>
        <namePart type="family">Eger</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2020-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Dan</namePart>
            <namePart type="family">Jurafsky</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Joyce</namePart>
            <namePart type="family">Chai</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Natalie</namePart>
            <namePart type="family">Schluter</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Joel</namePart>
            <namePart type="family">Tetreault</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Online</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>We study unsupervised multi-document summarization evaluation metrics, which require neither human-written reference summaries nor human annotations (e.g. preferences, ratings, etc.). We propose SUPERT, which rates the quality of a summary by measuring its semantic similarity with a pseudo reference summary, i.e. selected salient sentences from the source documents, using contextualized embeddings and soft token alignment techniques. Compared to the state-of-the-art unsupervised evaluation metrics, SUPERT correlates better with human ratings by 18- 39%. Furthermore, we use SUPERT as rewards to guide a neural-based reinforcement learning summarizer, yielding favorable performance compared to the state-of-the-art unsupervised summarizers. All source code is available at https://github.com/yg211/acl20-ref-free-eval.</abstract>
    <identifier type="citekey">gao-etal-2020-supert</identifier>
    <identifier type="doi">10.18653/v1/2020.acl-main.124</identifier>
    <location>
        <url>https://aclanthology.org/2020.acl-main.124</url>
    </location>
    <part>
        <date>2020-07</date>
        <extent unit="page">
            <start>1347</start>
            <end>1354</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T SUPERT: Towards New Frontiers in Unsupervised Evaluation Metrics for Multi-Document Summarization
%A Gao, Yang
%A Zhao, Wei
%A Eger, Steffen
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F gao-etal-2020-supert
%X We study unsupervised multi-document summarization evaluation metrics, which require neither human-written reference summaries nor human annotations (e.g. preferences, ratings, etc.). We propose SUPERT, which rates the quality of a summary by measuring its semantic similarity with a pseudo reference summary, i.e. selected salient sentences from the source documents, using contextualized embeddings and soft token alignment techniques. Compared to the state-of-the-art unsupervised evaluation metrics, SUPERT correlates better with human ratings by 18- 39%. Furthermore, we use SUPERT as rewards to guide a neural-based reinforcement learning summarizer, yielding favorable performance compared to the state-of-the-art unsupervised summarizers. All source code is available at https://github.com/yg211/acl20-ref-free-eval.
%R 10.18653/v1/2020.acl-main.124
%U https://aclanthology.org/2020.acl-main.124
%U https://doi.org/10.18653/v1/2020.acl-main.124
%P 1347-1354

Markdown (Informal)

[SUPERT: Towards New Frontiers in Unsupervised Evaluation Metrics for Multi-Document Summarization](https://aclanthology.org/2020.acl-main.124) (Gao et al., ACL 2020)

• SUPERT: Towards New Frontiers in Unsupervised Evaluation Metrics for Multi-Document Summarization (Gao et al., ACL 2020)

ACL

• Yang Gao, Wei Zhao, and Steffen Eger. 2020. SUPERT: Towards New Frontiers in Unsupervised Evaluation Metrics for Multi-Document Summarization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1347–1354, Online. Association for Computational Linguistics.