<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="kuptavanich-etal-2018-generating">
    <titleInfo>
        <title>Generating Summaries of Sets of Consumer Products: Learning from Experiments</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Kittipitch</namePart>
        <namePart type="family">Kuptavanich</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ehud</namePart>
        <namePart type="family">Reiter</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Kees</namePart>
        <namePart type="family">Van Deemter</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Advaith</namePart>
        <namePart type="family">Siddharthan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2018-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 11th International Conference on Natural Language Generation</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Emiel</namePart>
            <namePart type="family">Krahmer</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Albert</namePart>
            <namePart type="family">Gatt</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Martijn</namePart>
            <namePart type="family">Goudbeek</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Tilburg University, The Netherlands</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>We explored the task of creating a textual summary describing a large set of objects characterised by a small number of features using an e-commerce dataset. When a set of consumer products is large and varied, it can be difficult for a consumer to understand how the products in the set differ; consequently, it can be challenging to choose the most suitable product from the set. To assist consumers, we generated high-level summaries of product sets. Two generation algorithms are presented, discussed, and evaluated with human users. Our evaluation results suggest a positive contribution to consumers’ understanding of the domain.</abstract>
    <identifier type="citekey">kuptavanich-etal-2018-generating</identifier>
    <identifier type="doi">10.18653/v1/W18-6548</identifier>
    <location>
        <url>https://aclanthology.org/W18-6548</url>
    </location>
    <part>
        <date>2018-11</date>
        <extent unit="page">
            <start>403</start>
            <end>407</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Generating Summaries of Sets of Consumer Products: Learning from Experiments
%A Kuptavanich, Kittipitch
%A Reiter, Ehud
%A Van Deemter, Kees
%A Siddharthan, Advaith
%Y Krahmer, Emiel
%Y Gatt, Albert
%Y Goudbeek, Martijn
%S Proceedings of the 11th International Conference on Natural Language Generation
%D 2018
%8 November
%I Association for Computational Linguistics
%C Tilburg University, The Netherlands
%F kuptavanich-etal-2018-generating
%X We explored the task of creating a textual summary describing a large set of objects characterised by a small number of features using an e-commerce dataset. When a set of consumer products is large and varied, it can be difficult for a consumer to understand how the products in the set differ; consequently, it can be challenging to choose the most suitable product from the set. To assist consumers, we generated high-level summaries of product sets. Two generation algorithms are presented, discussed, and evaluated with human users. Our evaluation results suggest a positive contribution to consumers’ understanding of the domain.
%R 10.18653/v1/W18-6548
%U https://aclanthology.org/W18-6548
%U https://doi.org/10.18653/v1/W18-6548
%P 403-407

Markdown (Informal)

[Generating Summaries of Sets of Consumer Products: Learning from Experiments](https://aclanthology.org/W18-6548) (Kuptavanich et al., INLG 2018)

• Generating Summaries of Sets of Consumer Products: Learning from Experiments (Kuptavanich et al., INLG 2018)

ACL

• Kittipitch Kuptavanich, Ehud Reiter, Kees Van Deemter, and Advaith Siddharthan. 2018. Generating Summaries of Sets of Consumer Products: Learning from Experiments. In Proceedings of the 11th International Conference on Natural Language Generation, pages 403–407, Tilburg University, The Netherlands. Association for Computational Linguistics.