<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="jin-etal-2020-hooks">
    <titleInfo>
        <title>Hooks in the Headline: Learning to Generate Headlines with Controlled Styles</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Di</namePart>
        <namePart type="family">Jin</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Zhijing</namePart>
        <namePart type="family">Jin</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Joey</namePart>
        <namePart type="given">Tianyi</namePart>
        <namePart type="family">Zhou</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Lisa</namePart>
        <namePart type="family">Orii</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Peter</namePart>
        <namePart type="family">Szolovits</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>Current summarization systems only produce plain, factual headlines, far from the practical needs for the exposure and memorableness of the articles. We propose a new task, Stylistic Headline Generation (SHG), to enrich the headlines with three style options (humor, romance and clickbait), thus attracting more readers. With no style-specific article-headline pair (only a standard headline summarization dataset and mono-style corpora), our method TitleStylist generates stylistic headlines by combining the summarization and reconstruction tasks into a multitasking framework. We also introduced a novel parameter sharing scheme to further disentangle the style from text. Through both automatic and human evaluation, we demonstrate that TitleStylist can generate relevant, fluent headlines with three target styles: humor, romance, and clickbait. The attraction score of our model generated headlines outperforms the state-of-the-art summarization model by 9.68%, even outperforming human-written references.</abstract>
    <identifier type="citekey">jin-etal-2020-hooks</identifier>
    <identifier type="doi">10.18653/v1/2020.acl-main.456</identifier>
    <location>
        <url>https://aclanthology.org/2020.acl-main.456</url>
    </location>
    <part>
        <date>2020-07</date>
        <extent unit="page">
            <start>5082</start>
            <end>5093</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Hooks in the Headline: Learning to Generate Headlines with Controlled Styles
%A Jin, Di
%A Jin, Zhijing
%A Zhou, Joey Tianyi
%A Orii, Lisa
%A Szolovits, Peter
%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 jin-etal-2020-hooks
%X Current summarization systems only produce plain, factual headlines, far from the practical needs for the exposure and memorableness of the articles. We propose a new task, Stylistic Headline Generation (SHG), to enrich the headlines with three style options (humor, romance and clickbait), thus attracting more readers. With no style-specific article-headline pair (only a standard headline summarization dataset and mono-style corpora), our method TitleStylist generates stylistic headlines by combining the summarization and reconstruction tasks into a multitasking framework. We also introduced a novel parameter sharing scheme to further disentangle the style from text. Through both automatic and human evaluation, we demonstrate that TitleStylist can generate relevant, fluent headlines with three target styles: humor, romance, and clickbait. The attraction score of our model generated headlines outperforms the state-of-the-art summarization model by 9.68%, even outperforming human-written references.
%R 10.18653/v1/2020.acl-main.456
%U https://aclanthology.org/2020.acl-main.456
%U https://doi.org/10.18653/v1/2020.acl-main.456
%P 5082-5093

Markdown (Informal)

[Hooks in the Headline: Learning to Generate Headlines with Controlled Styles](https://aclanthology.org/2020.acl-main.456) (Jin et al., ACL 2020)

• Hooks in the Headline: Learning to Generate Headlines with Controlled Styles (Jin et al., ACL 2020)

ACL

• Di Jin, Zhijing Jin, Joey Tianyi Zhou, Lisa Orii, and Peter Szolovits. 2020. Hooks in the Headline: Learning to Generate Headlines with Controlled Styles. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5082–5093, Online. Association for Computational Linguistics.