An Evaluation of Two Commercial Deep Learning-Based Information Retrieval Systems for COVID-19 Literature

The COVID-19 pandemic has resulted in a tremendous need for access to the latest scientific information, primarily through the use of text mining and search tools. This has led to both corpora for biomedical articles related to COVID-19 (such as the CORD-19 corpus (Wang et al., 2020)) as well as search engines to query such data. While most research in search engines is performed in the academic field of information retrieval (IR), most academic search engines$\unicode{x2013}$though rigorously evaluated$\unicode{x2013}$are sparsely utilized, while major commercial web search engines (e.g., Google, Bing) dominate. This relates to COVID-19 because it can be expected that commercial search engines deployed for the pandemic will gain much higher traction than those produced in academic labs, and thus leads to questions about the empirical performance of these search tools. This paper seeks to empirically evaluate two such commercial search engines for COVID-19, produced by Google and Amazon, in comparison to the more academic prototypes evaluated in the context of the TREC-COVID track (Roberts et al., 2020). We performed several steps to reduce bias in the available manual judgments in order to ensure a fair comparison of the two systems with those submitted to TREC-COVID. We find that the top-performing system from TREC-COVID on bpref metric performed the best among the different systems evaluated in this study on all the metrics. This has implications for developing biomedical retrieval systems for future health crises as well as trust in popular health search engines.