  Biomedical research papers use significantly different language and jargon
when compared to typical English text, which reduces the utility of pre-trained
NLP models in this domain. Meanwhile Medline, a database of biomedical
abstracts, introduces nearly a million new documents per-year. Applications
that could benefit from understanding this wealth of publicly available
information, such as scientific writing assistants, chat-bots, or descriptive
hypothesis generation systems, require new domain-centered approaches. A
conditional language model, one that learns the probability of words given some
a priori criteria, is a fundamental building block in many such applications.
We propose a transformer-based conditional language model with a shallow
encoder "condition" stack, and a deep "language model" stack of multi-headed
attention blocks. The condition stack encodes metadata used to alter the output
probability distribution of the language model stack. We sample this
distribution in order to generate biomedical abstracts given only a proposed
title, an intended publication year, and a set of keywords. Using typical
natural language generation metrics, we demonstrate that this proposed approach
is more capable of producing non-trivial relevant entities within the abstract
body than the 1.5B parameter GPT-2 language model.
