Extracting Scientific Figures with Distantly Supervised Neural Networks
This work addresses the lack of labeled data for scientific figure extraction, enabling data-driven methods for researchers and search engines, though it is incremental as it builds on existing web collections and neural network techniques.
The authors tackled the problem of extracting scientific figures from documents by creating a large, automatically labeled dataset of over 5.5 million figures with 96.8% average precision, which they used to train a deep neural network deployed in Semantic Scholar to process 13 million documents.
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.