Evaluation of attention-based LSTM and Bi-LSTM networks for abstract text classification in systematic literature review automation.

Ofori-Boateng, Regina; Aceves-Martins, Magaly; Jayne, Chrisina; Wiratunga, Nirmalie; Moreno-Garcia, Carlos Francisco

Abstract

Systematic Review (SR) presents the highest form of evidence in research for decision and policy-making. Nonetheless, the structured steps involved in carrying out SRs make it demanding for reviewers. Many studies have projected the abstract screening stage in the SR process to be the most burdensome for reviewers, thus automating this stage with artificial intelligence (AI). However, majority of these studies focus on using traditional machine learning classifiers for the abstract classification. Thus, there remain a gap to explore the potential of deep learning techniques for this task. This study seeks to bridge the gap by exploring how LSTM and Bi-LSTM models together with GloVe for vectorisation can accelerate this stage. As a further aim to increase precision while sustaining a recall >= 95% due to precision-recall trade-off, attention mechanics is added to these classifiers. The final experimental results obtained showed that Bi-LSTM with attention has the capacity to expedite citation screening.

Citation

OFORI-BOATENG, R., ACEVES-MARTINS, M., JAYNE, C., WIRATUNGA, N. and MORENO-GARCIA, C.F. 2023. Evaluation of attention-based LSTM and Bi-LSTM networks for abstract text classification in systematic literature review automation. Porcedia computer science [online], 222: selected papers from the 2023 International Neural Network Society workshop on deep learning innovations and applications (INNS DLIA 2023), co-located with the 2023 International joint conference on neural networks (IJCNN), 18-23 June 2023, Gold Coast, Australia, pages 114-126. Available from: https://doi.org/10.1016/j.procs.2023.08.149

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2023 International Neural Network Society workshop on deep learning innovations and applications (INNS DLIA 2023)
• Start Date: Jun 18, 2023
• End Date: Jun 23, 2023
• Acceptance Date: Jun 12, 2023
• Online Publication Date: Aug 31, 2023
• Publication Date: Dec 31, 2023
• Deposit Date: Sep 1, 2023
• Publicly Available Date: Sep 1, 2023
• Journal: Procedia computer science
• Print ISSN: 1877-0509
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 222
• Pages: 114-126
• DOI: https://doi.org/10.1016/j.procs.2023.08.149
• Keywords: Systematic literature review; Abstract screening; Artificial intelligence; Machine learning; Deep learning; LSTMBi-LSTM
• Public URL: https://rgu-repository.worktribe.com/output/2054590