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

Can questions summarize a corpus? Using question generation for characterizing COVID-19 research