  Precisely defining the terminology is the first step in scientific
communication. Developing neural text generation models for definition
generation can circumvent the labor-intensity curation, further accelerating
scientific discovery. Unfortunately, the lack of large-scale terminology
definition dataset hinders the process toward definition generation. In this
paper, we present a large-scale terminology definition dataset Graphine
covering 2,010,648 terminology definition pairs, spanning 227 biomedical
subdisciplines. Terminologies in each subdiscipline further form a directed
acyclic graph, opening up new avenues for developing graph-aware text
generation models. We then proposed a novel graph-aware definition generation
model Graphex that integrates transformer with graph neural network. Our model
outperforms existing text generation models by exploiting the graph structure
of terminologies. We further demonstrated how Graphine can be used to evaluate
pretrained language models, compare graph representation learning methods and
predict sentence granularity. We envision Graphine to be a unique resource for
definition generation and many other NLP tasks in biomedicine.
