论文标题
DeepShovel：通过AI帮助
DeepShovel: An Online Collaborative Platform for Data Extraction in Geoscience Literature with AI Assistance
论文作者
论文摘要
地球科学家以及许多领域的研究人员都需要阅读大量文献来定位，提取和汇总相关的结果和数据，以实现未来的研究或构建科学数据库，但是没有现有的系统来支持此用例。在本文中，基于一项关于地球科学家如何合作注释文献，提取和汇总数据的形成性研究的结果，我们提出了DeepShovel，这是一种公开可用的AI-AI辅助数据提取系统来满足他们的需求。 DeepShovel利用最新的神经网络模型轻松，准确地注释研究人员（以PDF格式），并以人类协作方式从表，图形，图形等中提取数据。对14位研究人员进行的后续用户评估建议，DeepShovel提高了用户对构建科学数据库的数据提取效率，并鼓励团队建立更大的规模但更紧密耦合的协作。
Geoscientists, as well as researchers in many fields, need to read a huge amount of literature to locate, extract, and aggregate relevant results and data to enable future research or to build a scientific database, but there is no existing system to support this use case well. In this paper, based on the findings of a formative study about how geoscientists collaboratively annotate literature and extract and aggregate data, we proposed DeepShovel, a publicly-available AI-assisted data extraction system to support their needs. DeepShovel leverages the state-of-the-art neural network models to support researcher(s) easily and accurately annotate papers (in the PDF format) and extract data from tables, figures, maps, etc. in a human-AI collaboration manner. A follow-up user evaluation with 14 researchers suggested DeepShovel improved users' efficiency of data extraction for building scientific databases, and encouraged teams to form a larger scale but more tightly-coupled collaboration.
