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Text Mining in Scientific Literature Evaluation: Extraction of Keywords for Describing Content

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Abstract

Keywords should represent the content of documents in a compact form. They serve to assign suitable publications to a search query in the context of a scientific literature search. If a literature search is carried out as part of a scientific work, the found publications must be analyzed and their content evaluated. This can be a large number of publications, so that the analysis of the content can be extremely time-consuming. This article describes how the analysis of publications can be supported by text mining in the context of “Explainable AI” literature evaluation. Keywords are extracted from the abstracts of the found publications by text mining.

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Authors and Affiliations

  1. Homburg, Germany

    Thomas Barton

  2. Worms, Germany

    Arthur Kokoev

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  1. Thomas Barton
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  2. Arthur Kokoev
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Correspondence to Thomas Barton .

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Editors and Affiliations

  1. FB Informatik, Hochschule Worms, Worms, Germany

    Thomas Barton

  2. FB Wirtschaft, Informatik, Recht (WIR), Technische Hochschule Wildau, Wildau, Germany

    Christian Müller

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Barton, T., Kokoev, A. (2023). Text Mining in Scientific Literature Evaluation: Extraction of Keywords for Describing Content. In: Barton, T., Müller, C. (eds) Apply Data Science. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-38798-3_11

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  • DOI: https://doi.org/10.1007/978-3-658-38798-3_11

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  • Publisher Name: Springer Vieweg, Wiesbaden

  • Print ISBN: 978-3-658-38797-6

  • Online ISBN: 978-3-658-38798-3

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