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COVID-19 information retrieval with deep-learning based semantic search, question answering, and abstractive summarization

Computer Science

COVID-19 information retrieval with deep-learning based semantic search, question answering, and abstractive summarization

A. Esteva, A. Kale, et al.

Discover how CO-Search, developed by Andre Esteva and team, revolutionizes the way we navigate vast COVID-19 literature! This innovative semantic search engine merges powerful techniques like semantic-keyword retrieval, question answering, and summarization to sift through misinformation and deliver accurate insights.

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Playback language: English
Abstract
This paper introduces CO-Search, a semantic search engine designed to efficiently retrieve relevant information from the vast COVID-19 literature. CO-Search uses a multi-stage approach, combining a hybrid semantic-keyword retriever with a re-ranker that leverages question answering and abstractive summarization. The system achieves strong performance on the TREC-COVID challenge, demonstrating its effectiveness in handling complex queries and mitigating misinformation.
Publisher
npj Digital Medicine
Published On
Apr 12, 2021
Authors
Andre Esteva, Anuprit Kale, Romain Paulus, Kazuma Hashimoto, Wenpeng Yin, Dragomir Radev, Richard Socher
Tags
COVID-19
semantic search
information retrieval
misinformation
TREC-COVID
question answering
abstractive summarization

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