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

中文翻译：

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问题可以概括语料库吗？使用问题生成来表征 COVID-19 研究

一些文本数据的潜在问题是什么？在这项工作中，我们研究使用问题生成模型来探索文档集合。我们的方法，称为 corpus2question，包括在语料库上应用预训练的问题生成模型，并按频率和时间聚合生成的问题。该技术是主题建模和词云等方法的替代方法，用于汇总大量文本数据。结果表明，在与 COVID-19 相关的科学文章语料库上应用 corpus2question 会产生与该主题相关的问题。最常见的问题是“covid 19 是什么”和“covid 的治疗方法是什么”。在 1000 个最常见的问题中，“群体免疫的门槛是什么”和“