CITEWERTs: A System Combining Cite-Worthiness with Citation Recommendation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10772)


Due to the vast amount of publications appearing in the various scientific disciplines, there is a need for automatically recommending citations for text segments of scientific documents. Surprisingly, only few demonstrations of citation-based recommender systems have been proposed so far. Moreover, existing solutions either do not consider the raw textual context or they recommend citations for predefined citation contexts or just for whole documents. In contrast to them, we propose a novel two-step architecture: First, given some input text, our system determines for each potential citation context, which is typically a sentence long, if it is actually “cite-worthy.” When this is the case, secondly, our system recommends citations for that context. Given this architecture, in our demonstration we show how we can guide the user to only those sentences that deserve citations and how to present recommended citations for single sentences. In this way, we reduce the user’s need to review too many sentences and recommendations.


Citation recommendation Citation context Digital libraries Recommender systems 



Michael Färber is an International Research Fellow of the Japan Society for the Promotion of Science (JSPS). The work was partially supported by MIC SCOPE (171507010).


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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of FreiburgFreiburg im BreisgauGermany
  2. 2.Kyoto UniversityKyotoJapan

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