Conference paper Open Access

# What to Read Next? Challenges and Preliminary Results in Selecting Representative Documents

Beck, Tilman; Böschen, Falk; Scherp, Ansgar

### DataCite XML Export

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<identifier identifierType="URL">https://zenodo.org/record/1409662</identifier>
<creators>
<creator>
<creatorName>Beck, Tilman</creatorName>
<givenName>Tilman</givenName>
<familyName>Beck</familyName>
<affiliation>Department of Computer Science, Kiel University, Kiel, Germany</affiliation>
</creator>
<creator>
<creatorName>Böschen, Falk</creatorName>
<givenName>Falk</givenName>
<familyName>Böschen</familyName>
<nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-4223-5353</nameIdentifier>
<affiliation>Department of Computer Science, Kiel University, Kiel, Germany</affiliation>
</creator>
<creator>
<creatorName>Scherp, Ansgar</creatorName>
<givenName>Ansgar</givenName>
<familyName>Scherp</familyName>
<nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-2653-9245</nameIdentifier>
<affiliation>Computing Science and Mathematics, University of Stirling, Stirling, Scotland, UK</affiliation>
</creator>
</creators>
<titles>
<title>What to Read Next? Challenges and Preliminary Results in Selecting Representative Documents</title>
</titles>
<publisher>Zenodo</publisher>
<publicationYear>2018</publicationYear>
<subjects>
<subject>Representative document selection</subject>
<subject>Document clustering</subject>
</subjects>
<dates>
<date dateType="Issued">2018-08-07</date>
</dates>
<language>en</language>
<resourceType resourceTypeGeneral="ConferencePaper"/>
<alternateIdentifiers>
<alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/1409662</alternateIdentifier>
</alternateIdentifiers>
<relatedIdentifiers>
<relatedIdentifier relatedIdentifierType="DOI" relationType="IsIdenticalTo">10.1007/978-3-319-99133-7_19</relatedIdentifier>
<relatedIdentifier relatedIdentifierType="URL" relationType="IsPartOf">https://zenodo.org/communities/moving-h2020</relatedIdentifier>
</relatedIdentifiers>
<rightsList>
<rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
</rightsList>
<descriptions>
<description descriptionType="Abstract">&lt;p&gt;The vast amount of scientific literature poses a challenge when one is trying to understand a previously unknown topic. Selecting a representative subset of documents that covers most of the desired content can solve this challenge by presenting the user a small subset of documents. We build on existing research on representative subset extraction and apply it in an information retrieval setting. Our document selection process consists of three steps: computation of the document representations, clustering, and selection of documents. We implement and compare two different document representations, two different clustering algorithms, and three different selection methods using a coverage and a redundancy metric. We execute our 36 experiments on two datasets, with 10 sample queries each, from different domains. The results show that there is no clear favorite and that we need to ask the question whether coverage and redundancy are sufficient for evaluating representative subsets.&lt;/p&gt;</description>
</descriptions>
<fundingReferences>
<fundingReference>
<funderName>European Commission</funderName>
<funderIdentifier funderIdentifierType="Crossref Funder ID">10.13039/100010661</funderIdentifier>
<awardNumber awardURI="info:eu-repo/grantAgreement/EC/H2020/693092/">693092</awardNumber>
<awardTitle>Training towards a society of data-savvy information professionals to enable open leadership innovation</awardTitle>
</fundingReference>
</fundingReferences>
</resource>

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