Non-textual components such as charts, diagrams and tables provide key
information in many scientific documents, but the lack of large labeled
datasets has impeded the development of data-driven methods for scientific
figure extraction. In this paper, we induce high-quality training labels for
the task of figure extraction in a large number of scientific documents, with
no human intervention. To accomplish this we leverage the auxiliary data
provided in two large web collections of scientific documents (arXiv and
PubMed) to locate figures and their associated captions in the rasterized PDF.
We share the resulting dataset of over 5.5 million induced labels---4,000 times
larger than the previous largest figure extraction dataset---with an average
precision of 96.8%, to enable the development of modern data-driven methods for
this task. We use this dataset to train a deep neural network for end-to-end
figure detection, yielding a model that can be more easily extended to new
domains compared to previous work. The model was successfully deployed in
Semantic Scholar, a large-scale academic search engine, and used to extract
figures in 13 million scientific documents.

本文介绍了一种通过大规模科学文档数据集训练，实现无人干预的科学图表自动提取的方法，并获得了高精度模型以及数据集，成功用于 13 百万学术文献的提取。