<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="debaene-etal-2026-learning">
    <titleInfo>
        <title>Learning through News: Bridging the Gap between Algorithmic Recommendation and Human Curation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Florian</namePart>
        <namePart type="family">Debaene</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Loic</namePart>
        <namePart type="family">De Langhe</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Orphee</namePart>
        <namePart type="family">De Clercq</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Veronique</namePart>
        <namePart type="family">Hoste</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-05</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Fifteenth Language Resources and Evaluation Conference</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Stelios</namePart>
            <namePart type="family">Piperidis</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Núria</namePart>
            <namePart type="family">Bel</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Henk</namePart>
            <namePart type="family">van den Heuvel</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Nancy</namePart>
            <namePart type="family">Ide</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Simon</namePart>
            <namePart type="family">Krek</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Antonio</namePart>
            <namePart type="family">Toral</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>ELRA Language Resource Association</publisher>
            <place>
                <placeTerm type="text">Palma de Mallorca, Spain</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>News recommendation systems play a central role in how readers access and process current events. Most recommenders’ underlying algorithmic strategies, however, prioritize user engagement over comprehension, amplifying risks of misinformation and filter bubbles. This study investigates whether fine-grained content-based recommendation strategies favor human knowledge retention and explores how such a content-based recommendation can be operationalized using event coreference–based document modeling. To this purpose, we first measure the effect of manually curated content-based news recommendation on knowledge retention across five news topics with 126 Dutch speaking participants. Next, we investigate document retrieval by comparing a state-of-the-art event coreference resolution system for Dutch which recommends news articles based on event chains with a document similarity retrieval baseline using state-of-the-art embedding models in three increasingly more complex test settings. The results demonstrate that human-curated content-based recommendation can positively and significantly impact readers’ knowledge retention. Moreover, we show that a fine-grained coreference system can approach said level of human curation better than state-of-the-art document retrieval methods. In general, this holds potential for scalable, comprehension-oriented news recommendation.</abstract>
    <identifier type="citekey">debaene-etal-2026-learning</identifier>
    <identifier type="doi">10.63317/29rc2j4yc3un</identifier>
    <location>
        <url>https://aclanthology.org/2026.lrec-1.58/</url>
    </location>
    <part>
        <date>2026-05</date>
        <extent unit="page">
            <start>778</start>
            <end>794</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Learning through News: Bridging the Gap between Algorithmic Recommendation and Human Curation
%A Debaene, Florian
%A De Langhe, Loic
%A De Clercq, Orphee
%A Hoste, Veronique
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F debaene-etal-2026-learning
%X News recommendation systems play a central role in how readers access and process current events. Most recommenders’ underlying algorithmic strategies, however, prioritize user engagement over comprehension, amplifying risks of misinformation and filter bubbles. This study investigates whether fine-grained content-based recommendation strategies favor human knowledge retention and explores how such a content-based recommendation can be operationalized using event coreference–based document modeling. To this purpose, we first measure the effect of manually curated content-based news recommendation on knowledge retention across five news topics with 126 Dutch speaking participants. Next, we investigate document retrieval by comparing a state-of-the-art event coreference resolution system for Dutch which recommends news articles based on event chains with a document similarity retrieval baseline using state-of-the-art embedding models in three increasingly more complex test settings. The results demonstrate that human-curated content-based recommendation can positively and significantly impact readers’ knowledge retention. Moreover, we show that a fine-grained coreference system can approach said level of human curation better than state-of-the-art document retrieval methods. In general, this holds potential for scalable, comprehension-oriented news recommendation.
%R 10.63317/29rc2j4yc3un
%U https://aclanthology.org/2026.lrec-1.58/
%U https://doi.org/10.63317/29rc2j4yc3un
%P 778-794

Markdown (Informal)

[Learning through News: Bridging the Gap between Algorithmic Recommendation and Human Curation](https://aclanthology.org/2026.lrec-1.58/) (Debaene et al., LREC 2026)

• Learning through News: Bridging the Gap between Algorithmic Recommendation and Human Curation (Debaene et al., LREC 2026)

ACL

• Florian Debaene, Loic De Langhe, Orphee De Clercq, and Veronique Hoste. 2026. Learning through News: Bridging the Gap between Algorithmic Recommendation and Human Curation. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 778–794, Palma de Mallorca, Spain. ELRA Language Resource Association.