Automatic Text Summarization of COVID-19 Medical Research Articles using BERT and GPT-2

TL;DR

This paper leverages BERT and GPT-2 models to generate abstractive summaries of COVID-19 research articles, aiding medical professionals in quickly understanding key information from rapidly expanding literature.

Contribution

It introduces a novel application of pre-trained NLP models for automatic summarization of COVID-19 research articles, enhancing information accessibility for the medical community.

Findings

ROUGE scores demonstrate effective summarization quality

Summaries provide comprehensive information based on article keywords

Model aids in quick understanding of COVID-19 literature

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.