论文标题
Caire-Covid：一个问题回答和以查询为重点的多文章摘要系统，用于COVID-19学术信息管理
CAiRE-COVID: A Question Answering and Query-focused Multi-Document Summarization System for COVID-19 Scholarly Information Management
论文作者
论文摘要
我们提出了Caire-Covid，这是一个实时答案（QA）和多文章摘要系统，该系统赢得了Kaggle Covid-19中的10个任务之一，由医学专家评判，赢得了开放研究数据集挑战。我们的系统旨在应对挖掘Covid-19在Covid-19上发表的众多科学文章的最新挑战，通过回答社区的高优先级问题并总结与问题相关的信息相关的信息。它将信息提取与最先进的质量检查和以查询为中心的多文件摘要技术相结合，从而从现有文献中选择并突出显示并突出显示的证据摘要。我们还建议以查询为重点的抽象性和提取性多文件摘要方法，以提供与问题相关的更相关的信息。我们进一步进行定量实验，以显示每个模块各种指标的一致改进。我们已经启动了我们的网站Caire-Covid，以供医学界更广泛地使用，并为我们的系统开源代码，以引导其他研究进一步研究。
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.
