Literature Filtering for Systematic Reviews with Transformers
<|reference_start|>Literature Filtering for Systematic Reviews with Transformers: Identifying critical research within the growing body of academic work is an essential element of quality research. Systematic review processes, used in evidence-based medicine, formalise this as a procedure that must be followed in a research program. However, it comes with an increasing burden in terms of the time required to identify the important articles of research for a given topic. In this work, we develop a method for building a general-purpose filtering system that matches a research question, posed as a natural language description of the required content, against a candidate set of articles obtained via the application of broad search terms. Our results demonstrate that transformer models, pre-trained on biomedical literature then fine tuned for the specific task, offer a promising solution to this problem. The model can remove large volumes of irrelevant articles for most research questions.<|reference_end|>
@article{hawkins2024efficient,
    title={Efficient Systematic Reviews: Literature Filtering with Transformers &
  Transfer Learning},
    author={John Hawkins and David Tivey},
    journal={arXiv preprint arXiv:2405.20354},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.20354},
    primaryClass={cs.DL cs.AI cs.CL cs.LG}
}