A dataset of COVID-19-related scientific literature is compiled, combining the articles from several online libraries and selecting those with open access and full text available. Then, hierarchical nonnegative matrix factorization is used to organize literature related to the novel coronavirus into a tree structure that allows researchers to search for relevant literature based on detected topics. We discover eight major latent topics and 52 granular subtopics in the body of literature, related to vaccines, genetic structure and modeling of the disease and patient studies, as well as related diseases and virology. In order that our tool may help current researchers, an interactive website is created that organizes available literature using this hierarchical structure.

中文翻译：

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通过分层 NMF 进行基于 COVID-19 文献主题的搜索

编译了与 COVID-19 相关的科学文献数据集，结合了来自多个在线图书馆的文章，并选择了那些具有开放访问权限和全文可用的文章。然后，使用分层非负矩阵分解将与新型冠状病毒相关的文献组织成树状结构，使研究人员可以根据检测到的主题搜索相关文献。我们在文献中发现了 8 个主要的潜在主题和 52 个细粒度的子主题，与疫苗、疾病和患者研究的遗传结构和模型以及相关疾病和病毒学有关。为了让我们的工具可以帮助当前的研究人员，我们创建了一个交互式网站，使用这种层次结构组织可用的文献。