Automatic Aspect Extraction from Scientific Texts
Anna Marshalova Elena Bruches Tatiana Batura
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
\url{https://github.com/anna-marshalova/automatic-aspect-extraction-from-scientific-texts}.