@inproceedings{su-etal-2020-caire,
title = "{CA}i{RE}-{COVID}: A Question Answering and Query-focused Multi-Document Summarization System for {COVID}-19 Scholarly Information Management",
author = "Su, Dan and
Xu, Yan and
Yu, Tiezheng and
Siddique, Farhad Bin and
Barezi, Elham and
Fung, Pascale",
editor = "Verspoor, Karin and
Cohen, Kevin Bretonnel and
Conway, Michael and
de Bruijn, Berry and
Dredze, Mark and
Mihalcea, Rada and
Wallace, Byron",
booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
month = dec,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.nlpcovid19-2.14",
doi = "10.18653/v1/2020.nlpcovid19-2.14",
abstract = "We present CAiRE-COVID, a real-time question answering (QA) and multi-document summarization system, which won one of the 10 tasks in the Kaggle COVID-19 Open Research Dataset Challenge, judged by medical experts. Our system aims to tackle the recent challenge of mining the numerous scientific articles being published on COVID-19 by answering high priority questions from the community and summarizing salient question-related information. It combines information extraction with state-of-the-art QA and query-focused multi-document summarization techniques, selecting and highlighting evidence snippets from existing literature given a query. We also propose query-focused abstractive and extractive multi-document summarization methods, to provide more relevant information related to the question. We further conduct quantitative experiments that show consistent improvements on various metrics for each module. We have launched our website CAiRE-COVID for broader use by the medical community, and have open-sourced the code for our system, to bootstrap further study by other researches.",
}
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<abstract>We present CAiRE-COVID, a real-time question answering (QA) and multi-document summarization system, which won one of the 10 tasks in the Kaggle COVID-19 Open Research Dataset Challenge, judged by medical experts. Our system aims to tackle the recent challenge of mining the numerous scientific articles being published on COVID-19 by answering high priority questions from the community and summarizing salient question-related information. It combines information extraction with state-of-the-art QA and query-focused multi-document summarization techniques, selecting and highlighting evidence snippets from existing literature given a query. We also propose query-focused abstractive and extractive multi-document summarization methods, to provide more relevant information related to the question. We further conduct quantitative experiments that show consistent improvements on various metrics for each module. We have launched our website CAiRE-COVID for broader use by the medical community, and have open-sourced the code for our system, to bootstrap further study by other researches.</abstract>
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%0 Conference Proceedings
%T CAiRE-COVID: A Question Answering and Query-focused Multi-Document Summarization System for COVID-19 Scholarly Information Management
%A Su, Dan
%A Xu, Yan
%A Yu, Tiezheng
%A Siddique, Farhad Bin
%A Barezi, Elham
%A Fung, Pascale
%Y Verspoor, Karin
%Y Cohen, Kevin Bretonnel
%Y Conway, Michael
%Y de Bruijn, Berry
%Y Dredze, Mark
%Y Mihalcea, Rada
%Y Wallace, Byron
%S Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020
%D 2020
%8 December
%I Association for Computational Linguistics
%C Online
%F su-etal-2020-caire
%X We present CAiRE-COVID, a real-time question answering (QA) and multi-document summarization system, which won one of the 10 tasks in the Kaggle COVID-19 Open Research Dataset Challenge, judged by medical experts. Our system aims to tackle the recent challenge of mining the numerous scientific articles being published on COVID-19 by answering high priority questions from the community and summarizing salient question-related information. It combines information extraction with state-of-the-art QA and query-focused multi-document summarization techniques, selecting and highlighting evidence snippets from existing literature given a query. We also propose query-focused abstractive and extractive multi-document summarization methods, to provide more relevant information related to the question. We further conduct quantitative experiments that show consistent improvements on various metrics for each module. We have launched our website CAiRE-COVID for broader use by the medical community, and have open-sourced the code for our system, to bootstrap further study by other researches.
%R 10.18653/v1/2020.nlpcovid19-2.14
%U https://aclanthology.org/2020.nlpcovid19-2.14
%U https://doi.org/10.18653/v1/2020.nlpcovid19-2.14
Markdown (Informal)
[CAiRE-COVID: A Question Answering and Query-focused Multi-Document Summarization System for COVID-19 Scholarly Information Management](https://aclanthology.org/2020.nlpcovid19-2.14) (Su et al., NLP-COVID19 2020)
ACL