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.

我们开发了一种建立通用筛选系统的方法，该系统可以根据研究问题中描述的内容需求，将其与广泛搜索词应用所得到的候选文章进行匹配。我们的结果表明，预训练于生物医学文献并针对特定任务进行微调的转换器模型为解决这个问题提供了有希望的解决方案，能够过滤大量的无关文章。