Abstract
Most existing large-scale academic search engines are built to retrieve text-based information. However, there are no large-scale retrieval services for scientific figures and tables. One challenge for such services is understanding scientific figures' semantics, such as their types and purposes. A key obstacle is the need for datasets containing annotated scientific figures and tables, which can then be used for classification, question-answering, and auto-captioning. Here, we develop a pipeline that extracts figures and tables from the scientific literature and a deep-learning-based framework that classifies scientific figures using visual features. Using this pipeline, we built the first large-scale automatically annotated corpus, ACL-Fig, consisting of 112,052 scientific figures extracted from ~56K research papers in the ACL Anthology. The ACL-Fig-Pilot dataset contains 1,671 manually labeled scientific figures belonging to 19 categories. The dataset is accessible at this https URL under a CC BY-NC license.
Abstract (translated)
大部分现有的大型学术搜索引擎都是基于文本检索的,但缺乏对科学数据和表格的大型检索服务。这种服务的一个问题是理解科学数据的语义,例如其类型和用途。一个关键障碍是需要包含注释的科学数据和表格数据集,这些数据集可以用于分类、回答问题和自动标题。在这里,我们开发了一个从科学文献中抽取数据和表格的 pipeline,并构建了一个基于深度学习的框架,使用视觉特征进行分类。利用这个 pipeline,我们建立了第一个大规模自动注释的 Corpus,ACL-Fig,由在ACL Anthology中大约56,000篇论文中提取的112,052个科学数据组成。ACL-Fig- pilot 数据集包含1,671个手动标注的科学数据,属于19个分类。该数据集可以在这个 https URL 下以 CC BY-NC 许可证访问。
URL
https://arxiv.org/abs/2301.12293