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This report describes the participation of two Danish universities,University of Copenhagen and Aalborg University, in the international searchengine competition on COVID-19 (the 2020 TREC-COVID Challenge) organised by theU.S. National Institute of Standards and Technology (NIST) and its TextRetrieval Conference (TREC) division. The aim of the competition was to findthe best search engine strategy for retrieving precise biomedical scientificinformation on COVID-19 from the largest, at that point in time, dataset ofcurated scientific literature on COVID-19 -- the COVID-19 Open Research Dataset(CORD-19). CORD-19 was the result of a call to action to the tech community bythe U.S. White House in March 2020, and was shortly thereafter posted on Kaggleas an AI competition by the Allen Institute for AI, the Chan ZuckerbergInitiative, Georgetown University's Center for Security and EmergingTechnology, Microsoft, and the National Library of Medicine at the US NationalInstitutes of Health. CORD-19 contained over 200,000 scholarly articles (ofwhich more than 100,000 were with full text) about COVID-19, SARS-CoV-2, andrelated coronaviruses, gathered from curated biomedical sources. The TREC-COVIDchallenge asked for the best way to (a) retrieve accurate and precisescientific information, in response to some queries formulated by biomedicalexperts, and (b) rank this information decreasingly by its relevance to thequery.  In this document, we describe the TREC-COVID competition setup, ourparticipation to it, and our resulting reflections and lessons learned aboutthe state-of-art technology when faced with the acute task of retrievingprecise scientific information from a rapidly growing corpus of literature, inresponse to highly specialised queries, in the middle of a pandemic.

Denmark's Participation in the Search Engine TREC COVID-19 Challenge:  Lessons Learned about Searching for Precise Biomedical Scientific Information  on COVID-19