COVID-19 information retrieval with deep-learning based semantic search, question answering, and abstractive summarization
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. Throughout 2020, over 400,000 coronavirus-related publications have...
Guardado en:
Autores principales: | Andre Esteva, Anuprit Kale, Romain Paulus, Kazuma Hashimoto, Wenpeng Yin, Dragomir Radev, Richard Socher |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/b821725462d6408ea729067c04e6893f |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Finding a needle in the haystack: performing an in-depth literature search to answer a clinical question
por: George GS, et al.
Publicado: (2014) -
Question Dependent Recurrent Entity Network for Question Answering
por: Andrea Madotto, et al.
Publicado: (2017) -
ISCHEMIA Trial: Key Questions and Answers
por: Jose Lopez-Sendon, et al.
Publicado: (2021) -
Investigation of Pre-Trained Bidirectional Encoder Representations from Transformers Checkpoints for Indonesian Abstractive Text Summarization
por: Henry Lucky, et al.
Publicado: (2021) -
25 Questions & Answers on Health & Human Rights
por: World Health Organization