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

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About: CO-Search: COVID-19 Information Retrieval with Semantic Search, Question Answering, and Abstractive Summarization Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	CO-Search: COVID-19 Information Retrieval with Semantic Search, Question Answering, and Abstractive Summarization
Creator	Esteva, Andre Hashimoto, Kazuma Kale, Anuprit Paulus, Romain Radev, Dragomir »more»
source	ArXiv
abstract	The COVID-19 global pandemic has resulted in international efforts to understand, track, and mitigate the disease, yielding a significant corpus of COVID-19 and SARS-CoV-2-related publications across scientific disciplines. As of May 2020, 128,000 coronavirus-related publications have been collected through the COVID-19 Open Research Dataset Challenge. Here we present CO-Search, a retriever-ranker semantic search engine designed to handle complex queries over the COVID-19 literature, potentially aiding overburdened health workers in finding scientific answers during a time of crisis. The retriever is built from a Siamese-BERT encoder that is linearly composed with a TF-IDF vectorizer, and reciprocal-rank fused with a BM25 vectorizer. The ranker is composed of a multi-hop question-answering module, that together with a multi-paragraph abstractive summarizer adjust retriever scores. To account for the domain-specific and relatively limited dataset, we generate a bipartite graph of document paragraphs and citations, creating 1.3 million (citation title, paragraph) tuples for training the encoder. We evaluate our system on the data of the TREC-COVID information retrieval challenge. CO-Search obtains top performance on the datasets of the first and second rounds, across several key metrics: normalized discounted cumulative gain, precision, mean average precision, and binary preference.
has issue date	2020-06-17(xsd:dateTime)
has license	arxiv
sha1sum (hex)	27f0246890395d743252fd93282544c7efdec443
resource representing a document's title	CO-Search: COVID-19 Information Retrieval with Semantic Search, Question Answering, and Abstractive Summarization
resource representing a document's body	covid:27f0246890395d743252fd93282544c7efdec443#body_text
is schema:about of	named entity 'COVID-19' named entity 'composed' named entity 'yielding' named entity 'normalized' named entity 'COVID-19' »more»

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