SynFinTabs: A Dataset of Synthetic Financial Tables for Information and
  Table Extraction
Authors: name: Ethan Bradley, name: Muhammad Roman, name: Karen Rafferty, name: Barry Devereux

Table extraction from document images is a challenging AI problem, and
labelled data for many content domains is difficult to come by. Existing table
extraction datasets often focus on scientific tables due to the vast amount of
academic articles that are readily available, along with their source code.
However, there are significant layout and typographical differences between
tables found across scientific, financial, and other domains. Current datasets
often lack the words, and their positions, contained within the tables, instead
relying on unreliable OCR to extract these features for training modern machine
learning models on natural language processing tasks. Therefore, there is a
need for a more general method of obtaining labelled data. We present
SynFinTabs, a large-scale, labelled dataset of synthetic financial tables. Our
hope is that our method of generating these synthetic tables is transferable to
other domains. To demonstrate the effectiveness of our dataset in training
models to extract information from table images, we create FinTabQA, a layout
large language model trained on an extractive question-answering task. We test
our model using real-world financial tables and compare it to a
state-of-the-art generative model and discuss the results. We make the dataset,
model, and dataset generation code publicly available.