  The scientific community continues to publish an overwhelming amount of new
research related to COVID-19 on a daily basis, leading to much literature
without little to no attention. To aid the community in understanding the
rapidly flowing array of COVID-19 literature, we propose a novel BERT
architecture that provides a brief yet original summarization of lengthy
papers. The model continually learns on new data in online fashion while
minimizing catastrophic forgetting, thus fitting to the need of the community.
Benchmark and manual examination of its performance show that the model provide
a sound summary of new scientific literature.
