Efficient Systematic Reviews: Literature Filtering with Transformers & Transfer Learning

TL;DR

This paper presents a transformer-based approach, fine-tuned on biomedical literature, to efficiently filter relevant articles for systematic reviews, reducing the manual effort and time required in evidence-based research.

Contribution

It introduces a general-purpose filtering system using transfer learning with transformers tailored for biomedical literature to improve article selection in systematic reviews.

Findings

Transformer models effectively filter irrelevant articles.

Fine-tuning on biomedical literature enhances relevance detection.

Significant reduction in manual screening effort.

Abstract

Identifying critical research within the growing body of academic work is an intrinsic aspect of conducting 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, and 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. Furthermore, analysis of the specific research questions in our training data suggest natural avenues for further improvement.