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。

使用变换器进行系统性文献筛选

Literature Filtering for Systematic Reviews with Transformers

Over the last decade, Unmanned Aerial Vehicles (UAVs) have been extensively
used in many commercial applications due to their manageability and risk
avoidance. One of the main problems considered is the Mission Planning for
multiple UAVs, where a solution plan must be found satisfying the different
constraints of the problem. This problem has multiple variables that must be
optimized simultaneously, such as the makespan, the cost of the mission or the
risk. Therefore, the problem has a lot of possible optimal solutions, and the
operator must select the final solution to be executed among them. In order to
reduce the workload of the operator in this decision process, a Decision
Support System (DSS) becomes necessary. In this work, a DSS consisting of
ranking and filtering systems, which order and reduce the optimal solutions,
has been designed. With regard to the ranking system, a wide range of
Multi-Criteria Decision Making (MCDM) methods, including some fuzzy MCDM, are
compared on a multi-UAV mission planning scenario, in order to study which
method could fit better in a multi-UAV decision support system. Expert
operators have evaluated the solutions returned, and the results show, on the
one hand, that fuzzy methods generally achieve better average scores, and on
the other, that all of the tested methods perform better when the preferences
of the operators are biased towards a specific variable, and worse when their
preferences are balanced. For the filtering system, a similarity function based
on the proximity of the solutions has been designed, and on top of that, a
threshold is tuned empirically to decide how to filter solutions without losing
much of the hypervolume of the space of solutions.

设计了一个决策支持系统（DSS），包括排名和过滤系统，用于多个无人机任务规划场景，并比较了多个模糊多准则决策方法，结果表明，模糊方法通常具有更好的平均分数，并且当操作员偏好一个特定变量时，所有测试方法表现得更好，而在偏好平衡时表现较差。对于过滤系统，设计了一种基于解的相似性的相似函数，并根据经验确定了一个阈值，用于在不丢失解空间的大部分超体积的情况下决定如何过滤解。