Automatic Aspect Extraction from Scientific Texts
This addresses the challenge of efficiently processing scientific literature for researchers, though it is incremental as it applies existing methods to a new language and dataset.
The researchers tackled the problem of automatically extracting key aspects (Task, Contribution, Method, Conclusion) from Russian-language scientific texts to aid literature reviews, by creating a cross-domain annotated dataset and a baseline model based on multilingual BERT fine-tuned on this data, which showed generalization to new domains despite training on limited domains.
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}.