Abstract
With the COVID-19 pandemic, there is a growing urgency for medical community
to keep up with the accelerating growth in the new coronavirus-related
literature. As a result, the COVID-19 Open Research Dataset Challenge has
released a corpus of scholarly articles and is calling for machine learning
approaches to help bridging the gap between the researchers and the rapidly
growing publications. Here, we take advantage of the recent advances in
pre-trained NLP models, BERT and OpenAI GPT-2, to solve this challenge by
performing text summarization on this dataset. We evaluate the results using
ROUGE scores and visual inspection. Our model provides abstractive and
comprehensive information based on keywords extracted from the original
articles. Our work can help the the medical community, by providing succinct
summaries of articles for which the abstract are not already available.
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