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