CAiRE-COVID：面向COVID-19学术信息管理的问题检索和面向查询的多文档摘要系统
CAiRE-COVID: A Question Answering and Query-focused Multi-Document Summarization System for COVID-19 Scholarly Information Management
摘要：
我们介绍CAiRE-COVID，一个实时问答（QA）和多文档摘要系统，它赢得了Kaggle COVID-19开放研究数据集挑战赛的10个任务之一，由医学专家评判。我们的系统旨在通过回答社区的高优先级问题并总结与问题相关的重要信息，来应对最近挖掘大量关于COVID-19的科学文章的挑战。它将信息抽取与最先进的QA和以查询为中心的多文档摘要技术相结合，从现有文献中选择并突出显示证据片段。我们还提出了以查询为中心的抽象和提取的多文档摘要方法，以提供更多的相关信息的问题。我们进一步进行定量实验，显示每个模块的各种指标的一致改进。我们已经推出了我们的网站CAiRE-COVID，供医学界更广泛地使用，并开放了我们系统的代码，以引导其他研究人员进行进一步的研究。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
