<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="yang-etal-2026-recontextualizing">
    <titleInfo>
        <title>Recontextualizing Famous Quotes for Brand Slogan Generation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Ziao</namePart>
        <namePart type="family">Yang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Zizhang</namePart>
        <namePart type="family">Chen</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Lei</namePart>
        <namePart type="family">Zhang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Hongfu</namePart>
        <namePart type="family">Liu</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-10</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Hung-Yi</namePart>
            <namePart type="family">Lee</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Sunipa</namePart>
            <namePart type="family">Dev</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Jessy</namePart>
            <namePart type="family">Li</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Desmond</namePart>
            <namePart type="family">Elliott</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Budapest, Hungary</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-8-89176-459-0</identifier>
    </relatedItem>
    <abstract>Slogans are concise and memorable catchphrases that play crucial roles in advertising by conveying brand identity and shaping public perception. However, advertising fatigue reduces the effectiveness of repeated slogans, creating a growing demand for novel, creative, and insightful slogan generation. While recent work leverages large language models (LLMs) for this task, existing approaches often generate stylistically repetitive outputs that fall short in distinctiveness and insight.\par We argue that effective slogans should balance novelty with familiarity and propose a new paradigm that recontextualizes persona-related famous quotes for slogan generation. Well-known quotes naturally align with slogan-length text, employ rich rhetorical devices, and offer depth and insight, making them a powerful resource for creative generation. Technically, we introduce a framework that decomposes slogan generation into interpretable subtasks with a single-pass prompt, including quote matching, structural decomposition, vocabulary replacement, and remix generation. Extensive automatic and human evaluations demonstrate marginal improvements in diversity, emotional impact, and first-glance human preference over three state-of-the-art LLM baselines.</abstract>
    <identifier type="citekey">yang-etal-2026-recontextualizing</identifier>
    <location>
        <url>https://aclanthology.org/2026.emnlp-main.2535/</url>
    </location>
    <part>
        <date>2026-10</date>
        <extent unit="page">
            <start>57651</start>
            <end>57665</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Recontextualizing Famous Quotes for Brand Slogan Generation
%A Yang, Ziao
%A Chen, Zizhang
%A Zhang, Lei
%A Liu, Hongfu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F yang-etal-2026-recontextualizing
%X Slogans are concise and memorable catchphrases that play crucial roles in advertising by conveying brand identity and shaping public perception. However, advertising fatigue reduces the effectiveness of repeated slogans, creating a growing demand for novel, creative, and insightful slogan generation. While recent work leverages large language models (LLMs) for this task, existing approaches often generate stylistically repetitive outputs that fall short in distinctiveness and insight.\par We argue that effective slogans should balance novelty with familiarity and propose a new paradigm that recontextualizes persona-related famous quotes for slogan generation. Well-known quotes naturally align with slogan-length text, employ rich rhetorical devices, and offer depth and insight, making them a powerful resource for creative generation. Technically, we introduce a framework that decomposes slogan generation into interpretable subtasks with a single-pass prompt, including quote matching, structural decomposition, vocabulary replacement, and remix generation. Extensive automatic and human evaluations demonstrate marginal improvements in diversity, emotional impact, and first-glance human preference over three state-of-the-art LLM baselines.
%U https://aclanthology.org/2026.emnlp-main.2535/
%P 57651-57665

Markdown (Informal)

[Recontextualizing Famous Quotes for Brand Slogan Generation](https://aclanthology.org/2026.emnlp-main.2535/) (Yang et al., EMNLP 2026)

• Recontextualizing Famous Quotes for Brand Slogan Generation (Yang et al., EMNLP 2026)

ACL

• Ziao Yang, Zizhang Chen, Lei Zhang, and Hongfu Liu. 2026. Recontextualizing Famous Quotes for Brand Slogan Generation. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 57651–57665, Budapest, Hungary. Association for Computational Linguistics.