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The COVID-19 global pandemic has resulted in international efforts tounderstand, track, and mitigate the disease, yielding a significant corpus ofCOVID-19 and SARS-CoV-2-related publications across scientific disciplines. Asof May 2020, 128,000 coronavirus-related publications have been collectedthrough the COVID-19 Open Research Dataset Challenge. Here we presentCO-Search, a retriever-ranker semantic search engine designed to handle complexqueries over the COVID-19 literature, potentially aiding overburdened healthworkers in finding scientific answers during a time of crisis. The retriever isbuilt from a Siamese-BERT encoder that is linearly composed with a TF-IDFvectorizer, and reciprocal-rank fused with a BM25 vectorizer. The ranker iscomposed of a multi-hop question-answering module, that together with amulti-paragraph abstractive summarizer adjust retriever scores. To account forthe domain-specific and relatively limited dataset, we generate a bipartitegraph of document paragraphs and citations, creating 1.3 million (citationtitle, paragraph) tuples for training the encoder. We evaluate our system onthe data of the TREC-COVID information retrieval challenge. CO-Search obtainstop performance on the datasets of the first and second rounds, across severalkey metrics: normalized discounted cumulative gain, precision, mean averageprecision, and binary preference.

CO-Search: COVID-19 Information Retrieval with Semantic Search, Question Answering, and Abstractive Summarization