<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="gabryszak-etal-2024-enhancing-editorial">
    <titleInfo>
        <title>Enhancing Editorial Tasks: A Case Study on Rewriting Customer Help Page Contents Using Large Language Models</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Aleksandra</namePart>
        <namePart type="family">Gabryszak</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Daniel</namePart>
        <namePart type="family">Röder</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Arne</namePart>
        <namePart type="family">Binder</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Luca</namePart>
        <namePart type="family">Sion</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Leonhard</namePart>
        <namePart type="family">Hennig</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2024-09</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 17th International Natural Language Generation Conference</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Saad</namePart>
            <namePart type="family">Mahamood</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Nguyen</namePart>
            <namePart type="given">Le</namePart>
            <namePart type="family">Minh</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Daphne</namePart>
            <namePart type="family">Ippolito</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Tokyo, Japan</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>In this paper, we investigate the use of large language models (LLMs) to enhance the editorial process of rewriting customer help pages. We introduce a German-language dataset comprising Frequently Asked Question-Answer pairs, presenting both raw drafts and their revisions by professional editors. On this dataset, we evaluate the performance of four large language models (LLM) through diverse prompts tailored for the rewriting task. We conduct automatic evaluations of content and text quality using ROUGE, BERTScore, and ChatGPT. Furthermore, we let professional editors assess the helpfulness of automatically generated FAQ revisions for editorial enhancement. Our findings indicate that LLMs can produce FAQ reformulations beneficial to the editorial process. We observe minimal performance discrepancies among LLMs for this task, and our survey on helpfulness underscores the subjective nature of editors’ perspectives on editorial refinement.</abstract>
    <identifier type="citekey">gabryszak-etal-2024-enhancing-editorial</identifier>
    <location>
        <url>https://aclanthology.org/2024.inlg-main.33</url>
    </location>
    <part>
        <date>2024-09</date>
        <extent unit="page">
            <start>402</start>
            <end>411</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Enhancing Editorial Tasks: A Case Study on Rewriting Customer Help Page Contents Using Large Language Models
%A Gabryszak, Aleksandra
%A Röder, Daniel
%A Binder, Arne
%A Sion, Luca
%A Hennig, Leonhard
%Y Mahamood, Saad
%Y Minh, Nguyen Le
%Y Ippolito, Daphne
%S Proceedings of the 17th International Natural Language Generation Conference
%D 2024
%8 September
%I Association for Computational Linguistics
%C Tokyo, Japan
%F gabryszak-etal-2024-enhancing-editorial
%X In this paper, we investigate the use of large language models (LLMs) to enhance the editorial process of rewriting customer help pages. We introduce a German-language dataset comprising Frequently Asked Question-Answer pairs, presenting both raw drafts and their revisions by professional editors. On this dataset, we evaluate the performance of four large language models (LLM) through diverse prompts tailored for the rewriting task. We conduct automatic evaluations of content and text quality using ROUGE, BERTScore, and ChatGPT. Furthermore, we let professional editors assess the helpfulness of automatically generated FAQ revisions for editorial enhancement. Our findings indicate that LLMs can produce FAQ reformulations beneficial to the editorial process. We observe minimal performance discrepancies among LLMs for this task, and our survey on helpfulness underscores the subjective nature of editors’ perspectives on editorial refinement.
%U https://aclanthology.org/2024.inlg-main.33
%P 402-411

Markdown (Informal)

[Enhancing Editorial Tasks: A Case Study on Rewriting Customer Help Page Contents Using Large Language Models](https://aclanthology.org/2024.inlg-main.33) (Gabryszak et al., INLG 2024)

• Enhancing Editorial Tasks: A Case Study on Rewriting Customer Help Page Contents Using Large Language Models (Gabryszak et al., INLG 2024)

ACL

• Aleksandra Gabryszak, Daniel Röder, Arne Binder, Luca Sion, and Leonhard Hennig. 2024. Enhancing Editorial Tasks: A Case Study on Rewriting Customer Help Page Contents Using Large Language Models. In Proceedings of the 17th International Natural Language Generation Conference, pages 402–411, Tokyo, Japan. Association for Computational Linguistics.