  The abstract of a scientific paper distills the contents of the paper into a
short paragraph. In the biomedical literature, it is customary to structure an
abstract into discourse categories like BACKGROUND, OBJECTIVE, METHOD, RESULT,
and CONCLUSION, but this segmentation is uncommon in other fields like computer
science. Explicit categories could be helpful for more granular, that is,
discourse-level search and recommendation. The sparsity of labeled data makes
it challenging to construct supervised machine learning solutions for automatic
discourse-level segmentation of abstracts in non-bio domains. In this paper, we
address this problem using transfer learning. In particular, we define three
discourse categories BACKGROUND, TECHNIQUE, OBSERVATION-for an abstract because
these three categories are the most common. We train a deep neural network on
structured abstracts from PubMed, then fine-tune it on a small hand-labeled
corpus of computer science papers. We observe an accuracy of 75% on the test
corpus. We perform an ablation study to highlight the roles of the different
parts of the model. Our method appears to be a promising solution to the
automatic segmentation of abstracts, where the labeled data is sparse.
