  Claims are a fundamental unit of scientific discourse. The exponential growth
in the number of scientific publications makes automatic claim extraction an
important problem for researchers who are overwhelmed by this information
overload. Such an automated claim extraction system is useful for both manual
and programmatic exploration of scientific knowledge. In this paper, we
introduce a new dataset of 1,500 scientific abstracts from the biomedical
domain with expert annotations for each sentence indicating whether the
sentence presents a scientific claim. We introduce a new model for claim
extraction and compare it to several baseline models including rule-based and
deep learning techniques. Moreover, we show that using a transfer learning
approach with a fine-tuning step allows us to improve performance from a large
discourse-annotated dataset. Our final model increases F1-score by over 14
percent points compared to a baseline model without transfer learning. We
release a publicly accessible tool for discourse and claims prediction along
with an annotation tool. We discuss further applications beyond biomedical
literature.
