Automatic Aspect Extraction from Scientific Texts

TL;DR

This paper introduces a tool for automatic extraction of key aspects from Russian scientific texts across domains, utilizing a fine-tuned multilingual BERT model and a new annotated dataset.

Contribution

It presents a cross-domain dataset and a baseline BERT-based model for aspect extraction in Russian scientific texts, demonstrating cross-domain generalization.

Findings

Model generalizes across scientific domains

Annotated dataset covers multiple aspect types

Baseline results establish a starting point for future research

Abstract

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}.