Journal Article: “Citation Recommendation: Approaches and Datasets”

The full text article linked to below was published today (August 11, 2020) in International Journal on Digital Libraries.

Title

Citation Recommendation: Approaches and Datasets

Authors

Michael Färber
Karlsruhe Institute of Technology (KIT), Germany

Adam Jatowt
Kyoto University, Yoshida-Honmachi, Japan

Source

International Journal on Digital Libraries
DOI: 10.1007/s00799-020-00288-2

Abstract

Citation recommendation describes the task of recommending citations for a given text. Due to the overload of published scientific works in recent years on the one hand, and the need to cite the most appropriate publications when writing scientific texts on the other hand, citation recommendation has emerged as an important research topic. In recent years, several approaches and evaluation data sets have been presented. However, to the best of our knowledge, no literature survey has been conducted explicitly on citation recommendation. In this article, we give a thorough introduction to automatic citation recommendation research. We then present an overview of the approaches and data sets for citation recommendation and identify differences and commonalities using various dimensions. Last but not least, we shed light on the evaluation methods and outline general challenges in the evaluation and how to meet them. We restrict ourselves to citation recommendation for scientific publications, as this document type has been studied the most in this area. However, many of the observations and discussions included in this survey are also applicable to other types of text, such as news articles and encyclopedic articles.

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