摘要：One of the efects ofCOVID-19 pandemic is a rapidly growing and changing stream of publications to inform clinicians, researchers, policy makers, and patients about the health, socio-economic, and cultural consequences of the pandemic. Managing this information stream manually is not feasible. automatic Question answering can quickly bring the most salient points to the user’s attention. Leveraging a collection of scientifc articles, government websites, relevant news articles, curated social media posts, and questions asked by researchers, clinicians, and the general public, we developed a dataset to explore automatic Question answering for multiple stakeholders. analysis of questions asked by various stakeholders shows that while information needs of experts and the public may overlap, satisfactory answers to these questions often originate from diferent information sources or beneft from diferent approaches to answer generation . We believe that this dataset has the potential to support the development of question answering systems not only for epidemic questions, but for other domains with varying expertise such as legal or fnance .
