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scite: a smart citation index that displays the context of citations and classifies their intent using deep learning

View ORCID ProfileJ.M. Nicholson, M. Mordaunt, P. Lopez, View ORCID ProfileA. Uppala, View ORCID ProfileD. Rosati, N.P. Rodrigues, View ORCID ProfileP. Grabitz, View ORCID ProfileS.C. Rife
doi: https://doi.org/10.1101/2021.03.15.435418
J.M. Nicholson
1scite, Brooklyn, NY, USA
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  • For correspondence: josh@scite.ai
M. Mordaunt
1scite, Brooklyn, NY, USA
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P. Lopez
1scite, Brooklyn, NY, USA
2science-miner, France
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A. Uppala
1scite, Brooklyn, NY, USA
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D. Rosati
1scite, Brooklyn, NY, USA
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N.P. Rodrigues
1scite, Brooklyn, NY, USA
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P. Grabitz
1scite, Brooklyn, NY, USA
3Charite Universitaetsmedizin Berlin, Berlin, Germany
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S.C. Rife
1scite, Brooklyn, NY, USA
4Murray State University, Murray, KY, USA
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  • ORCID record for S.C. Rife
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Abstract

Citation indices are tools used by the academic community for research and research evaluation which aggregate scientific literature output and measure scientific impact by collating citation counts. Citation indices help measure the interconnections between scientific papers but fall short because they only display paper titles, authors, and the date of publications, and fail to communicate contextual information about why a citation was made. The usage of citations in research evaluation without due consideration to context can be problematic, if only because a citation that disputes a paper is treated the same as a citation that supports it. To solve this problem, we have used machine learning and other techniques to develop a “smart citation index” called scite, which categorizes citations based on context. Scite shows how a citation was used by displaying the surrounding textual context from the citing paper, and a classification from our deep learning model that indicates whether the statement provides supporting or disputing evidence for a referenced work, or simply mentions it. Scite has been developed by analyzing over 23 million full-text scientific articles and currently has a database of more than 800 million classified citation statements. Here we describe how scite works and how it can be used to further research and research evaluation.

Competing Interest Statement

The authors are shareholders and/or consultants or employees of Scite Inc.

Footnotes

  • https://github.com/kermitt2/grobid

  • https://github.com/kermitt2/biblio-glutton

  • https://github.com/kermitt2/delft

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted March 16, 2021.
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scite: a smart citation index that displays the context of citations and classifies their intent using deep learning
J.M. Nicholson, M. Mordaunt, P. Lopez, A. Uppala, D. Rosati, N.P. Rodrigues, P. Grabitz, S.C. Rife
bioRxiv 2021.03.15.435418; doi: https://doi.org/10.1101/2021.03.15.435418
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scite: a smart citation index that displays the context of citations and classifies their intent using deep learning
J.M. Nicholson, M. Mordaunt, P. Lopez, A. Uppala, D. Rosati, N.P. Rodrigues, P. Grabitz, S.C. Rife
bioRxiv 2021.03.15.435418; doi: https://doi.org/10.1101/2021.03.15.435418

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