  What are the latent questions on some textual data? In this work, we
investigate using question generation models for exploring a collection of
documents. Our method, dubbed corpus2question, consists of applying a
pre-trained question generation model over a corpus and aggregating the
resulting questions by frequency and time. This technique is an alternative to
methods such as topic modelling and word cloud for summarizing large amounts of
textual data. Results show that applying corpus2question on a corpus of
scientific articles related to COVID-19 yields relevant questions about the
topic. The most frequent questions are "what is covid 19" and "what is the
treatment for covid". Among the 1000 most frequent questions are "what is the
threshold for herd immunity" and "what is the role of ace2 in viral entry". We
show that the proposed method generated similar questions for 13 of the 27
expert-made questions from the CovidQA question answering dataset.
  The code to reproduce our experiments and the generated questions are
available at: https://github.com/unicamp-dl/corpus2question
