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

  • Tilman Beck
  • Falk BöschenEmail author
  • Ansgar Scherp
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 903)


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.


Representative document selection Document clustering 



This research was co-financed by the EU H2020 project MOVING ( under contract no 693092 and the EU project DigitalChampions_SH.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceKiel UniversityKielGermany
  2. 2.Computing Science and MathematicsUniversity of StirlingStirlingScotland, UK

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