  Learning semantically meaningful representations from scientific documents
can facilitate academic literature search and improve performance of
recommendation systems. Pre-trained language models have been shown to learn
rich textual representations, yet they cannot provide powerful document-level
representations for scientific articles. We propose MIReAD, a simple method
that learns high-quality representations of scientific papers by fine-tuning
transformer model to predict the target journal class based on the abstract. We
train MIReAD on more than 500,000 PubMed and arXiv abstracts across over 2,000
journal classes. We show that MIReAD produces representations that can be used
for similar papers retrieval, topic categorization and literature search. Our
proposed approach outperforms six existing models for representation learning
on scientific documents across four evaluation standards.
