  The world has faced the devastating outbreak of Severe Acute Respiratory
Syndrome Coronavirus-2 (SARS-CoV-2), or COVID-19, in 2020. Research in the
subject matter was fast-tracked to such a point that scientists were struggling
to keep up with new findings. With this increase in the scientific literature,
there arose a need for organizing those documents. We describe an approach to
organize and visualize the scientific literature on or related to COVID-19
using machine learning techniques so that papers on similar topics are grouped
together. By doing so, the navigation of topics and related papers is
simplified. We implemented this approach using the widely recognized CORD-19
dataset to present a publicly available proof of concept.
