  Scientific articles are long text documents organized into sections, each
describing aspects of the research. Analyzing scientific production has become
progressively challenging due to the increase in the number of available
articles. Within this scenario, our approach consisted of fine-tuning
transformer language models to generate sentence-level embeddings from
scientific articles, considering the following labels: background, objective,
methods, results, and conclusion. We trained our models on three datasets with
contrastive learning. Two datasets are from the article's abstracts in the
computer science and medical domains. Also, we introduce PMC-Sents-FULL, a
novel dataset of sentences extracted from the full texts of medical articles.
We compare the fine-tuned and baseline models in clustering and classification
tasks to evaluate our approach. On average, clustering agreement measures
values were five times higher. For the classification measures, in the
best-case scenario, we had an average improvement in F1-micro of 30.73\%.
Results show that fine-tuning sentence transformers with contrastive learning
and using the generated embeddings in downstream tasks is a feasible approach
to sentence classification in scientific articles. Our experiment codes are
available on GitHub.
