title: SynFinTabs: A Dataset of Synthetic Financial Tables for Information and
    Table Extraction
summary: 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.
paper_authors._id: 6752b7fa706d6371eb7ff47d
paper_authors.name: Ethan Bradley
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-06T10:37:51.109Z
paper_authors._id: 6262ce003662a749162c08ed
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6262ce003662a749162c08ed/NuY6APYss9cXwuFC-CpFT.jpeg
paper_authors.fullname: Ethan Bradley
paper_authors.type: user
paper_authors.user: ethanbradley
paper_authors._id: 6752b7fa706d6371eb7ff47e
paper_authors.name: Muhammad Roman
paper_authors._id: 6752b7fa706d6371eb7ff47f
paper_authors.name: Karen Rafferty
paper_authors._id: 6752b7fa706d6371eb7ff480
paper_authors.name: Barry Devereux
paper_title: SynFinTabs: A Dataset of Synthetic Financial Tables for Information and
    Table Extraction
paper_summary: 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.
paper_ai_summary: SynFinTabs is a large-scale synthetic financial tables dataset used to train FinTabQA, a layout-aware large language model for table image information extraction.
paper_ai_keywords: synthetic financial tables large language model extractive question-answering task