Being able to extract from scientific papers their main points, key insights,
and other important information, referred to here as aspects, might facilitate
the process of conducting a scientific literature review. Therefore, the aim of
our research is to create a tool for automatic aspect extraction from
Russian-language scientific texts of any domain. In this paper, we present a
cross-domain dataset of scientific texts in Russian, annotated with such
aspects as Task, Contribution, Method, and Conclusion, as well as a baseline
algorithm for aspect extraction, based on the multilingual BERT model
fine-tuned on our data. We show that there are some differences in aspect
representation in different domains, but even though our model was trained on a
limited number of scientific domains, it is still able to generalize to new
domains, as was proved by cross-domain experiments. The code and the dataset
are available at
https://github.com/anna-marshalova/automatic-aspect-extraction-from-scientific-texts.

我们的研究旨在创建一个用于从任何领域的俄语科技文本中自动提取方面的工具。本文提供了一份跨领域的俄文科技文本数据集，标注了任务、贡献、方法和结论等方面，并基于经过细调的多语言 BERT 模型提出了一个基准算法用于方面提取。我们展示了不同领域中方面表征存在一些差异，但即使我们的模型仅在有限数量的科技领域进行了训练，它仍能够推广到新的领域，这一点已通过跨领域实验证明。代码和数据集可在 https://github.com/anna-marshalova/automatic-aspect-extraction-from-scientific-texts 上获得。