  The pandemic has accelerated the pace at which COVID-19 scientific papers are
published. In addition, the process of manually assigning semantic indexes to
these papers by experts is even more time-consuming and overwhelming in the
current health crisis. Therefore, there is an urgent need for automatic
semantic indexing models which can effectively scale-up to newly introduced
concepts and rapidly evolving distributions of the hyperfocused related
literature. In this research, we present a novel semantic indexing approach
based on the state-of-the-art self-supervised representation learning and
transformer encoding exclusively suitable for pandemic crises. We present a
case study on a novel dataset that is based on COVID-19 papers published and
manually indexed in PubMed. Our study shows that our self-supervised model
outperforms the best performing models of BioASQ Task 8a by micro-F1 score of
0.1 and LCA-F score of 0.08 on average. Our model also shows superior
performance on detecting the supplementary concepts which is quite important
when the focus of the literature has drastically shifted towards specific
concepts related to the pandemic. Our study sheds light on the main challenges
confronting semantic indexing models during a pandemic, namely new domains and
drastic changes of their distributions, and as a superior alternative for such
situations, propose a model founded on approaches which have shown auspicious
performance in improving generalization and data efficiency in various NLP
tasks. We also show the joint indexing of major Medical Subject Headings (MeSH)
and supplementary concepts improves the overall performance.

Information Retrieval;Computation and Language;Machine Learning