Deep contextualized embeddings for quantifying the informative content in biomedical text summarization.

Journal: Computer methods and programs in biomedicine
Published Date: Oct 4, 2019

Abstract

BACKGROUND AND OBJECTIVE: Capturing the context of text is a challenging task in biomedical text summarization. The objective of this research is to show how contextualized embeddings produced by a deep bidirectional language model can be utilized to quantify the informative content of sentences in biomedical text summarization.

Authors

  • Milad Moradi
    Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran. Electronic address: milad.moradi@ec.iut.ac.ir.
  • Georg Dorffner
    Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna, Spitalgasse 23, 1090, Vienna, Austria.
  • Matthias Samwald
    Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Spitalgasse 23, 1090, Vienna, Austria. matthias.samwald@meduniwien.ac.at.

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