Leveraging deep learning for automatic literature screening in intelligent bibliometrics

Xieling CHEN, Haoran XIE*, Zongxi LI, Dian ZHANG, Gary CHENG, Fu Lee WANG, Hong-Ning DAI, Qing LI

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

3 Citations (Scopus)


Intelligent bibliometrics, by providing sufficient statistical information based on large-scale literature data analytics, is promising for understanding innovative pathways, addressing meaningful insights with the assistance of expert knowledge, and indicating key areas of scientific inquiry. However, the exponential growth of global scientific publication output in most areas of modern science makes it extremely difficult and labor-intensive to analyze literature in large volumes. This study aims to accelerate intelligent bibliometrics-driven literature analysis by leveraging deep learning for automatic literature screening. The comparison of different machine learning algorithms for the automatic classification of literature regarding relevance to a given research topic reveals the outstanding performance of deep learning. This study also compares different features as model input and provides suggestions about training dataset size. By leveraging deep learning’s abilities in predictive and big data analytics, this study makes contributions to intelligent bibliometrics by promoting literature screening and is promising to track technological changes and scientific evolutionary pathways.
Original languageEnglish
Pages (from-to)1483-1525
Number of pages43
JournalInternational Journal of Machine Learning and Cybernetics
Issue number4
Early online date15 Dec 2022
Publication statusPublished - Apr 2023

Bibliographical note

Funding Information:
The research described in this article has been supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (UGC/FDS16/E01/19), the Direct Grant (DR22A2) and the Faculty Research Grants (DB22B4 and DB22B7) of Lingnan University, Hong Kong, the One-off Special Fund from Central and Faculty Fund in Support of Research from 2019/20 to 2021/22 (MIT02/19-20), and Interdisciplinary Research Scheme of Dean’s Research Fund 2021/22 (FLASS/DRF/IDS-3) of The Education University of Hong Kong.

Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.


  • Automatic literature screening
  • Big data analytics
  • Deep neural networks
  • Intelligent bibliometrics


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