GFTE：基于图的金融表抽取
GFTE: Graph-based Financial Table Extraction
摘要：
表格数据是一种重要的信息表达形式，它可以将数据组织成标准的结构，便于信息检索和比较。然而，在金融行业和许多其他领域的表格往往是非结构化的数字文件，如便携式文档格式（PDF）和图像，这是很难直接提取。为了便于基于深度学习的非结构化数字文件中的表格提取，我们发布了一个名为FinTab的标准中文数据集，其中包含1,600多个不同类型的金融表格及其相应的JSON结构表示。此外，我们提出了一种新的基于图的卷积神经网络模型命名为GFTE作为未来比较的基线。GFTE将图像特征、位置特征和文本特征结合起来进行精确的边缘预测，取得了较好的效果。
英文摘要：
Tabular data is a crucial form of information expression, which can organize data in a standard structure for easy information retrieval and comparison. However, in financial industry and many other fields tables are often disclosed in unstructured digital files, e.g. Portable Document Format (PDF) and images, which are difficult to be extracted directly. In this paper, to facilitate deep learning based table extraction from unstructured digital files, we publish a standard Chinese dataset named FinTab, which contains more than 1,600 financial tables of diverse kinds and their corresponding structure representation in JSON. In addition, we propose a novel graph-based convolutional neural network model named GFTE as a baseline for future comparison. GFTE integrates image feature, position feature and textual feature together for precise edge prediction and reaches overall good results.
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。
