With an ever-increasing number of scientific papers published each year, it
becomes more difficult for researchers to explore a field that they are not
closely familiar with already. This greatly inhibits the potential for
cross-disciplinary research. A traditional introduction into an area may come
in the form of a review paper. However, not all areas and sub-areas have a
current review. In this paper, we present a method for the automatic generation
of a review paper corresponding to a user-defined query. This method consists
of two main parts. The first part identifies key papers in the area by their
bibliometric parameters, such as a graph of co-citations. The second stage uses
a BERT based architecture that we train on existing reviews for extractive
summarization of these key papers. We describe the general pipeline of our
method and some implementation details and present both automatic and expert
evaluations on the PubMed dataset.

本文介绍了一种方法，可以根据用户定义的查询自动生成一篇综述论文。该方法包括两个主要部分，第一部分通过诸如同袍引用的图表等文献计量参数来识别领域中的关键论文。第二阶段使用 BERT 架构，我们在现有的综述论文中对这些关键论文进行抽取式摘要。我们介绍了我们方法的一般流程和一些实现细节，并在 PubMed 数据集上进行了自动和专家评估。