LongFin: A Multimodal Document Understanding Model for Long Financial
  Domain Documents
Ahmed Masry Amir Hajian
Document AI is a growing research field that focuses on the comprehension and
extraction of information from scanned and digital documents to make everyday
business operations more efficient. Numerous downstream tasks and datasets have
been introduced to facilitate the training of AI models capable of parsing and
extracting information from various document types such as receipts and scanned
forms. Despite these advancements, both existing datasets and models fail to
address critical challenges that arise in industrial contexts. Existing
datasets primarily comprise short documents consisting of a single page, while
existing models are constrained by a limited maximum length, often set at 512
tokens. Consequently, the practical application of these methods in financial
services, where documents can span multiple pages, is severely impeded. To
overcome these challenges, we introduce LongFin, a multimodal document AI model
capable of encoding up to 4K tokens. We also propose the LongForms dataset, a
comprehensive financial dataset that encapsulates several industrial challenges
in financial documents. Through an extensive evaluation, we demonstrate the
effectiveness of the LongFin model on the LongForms dataset, surpassing the
performance of existing public models while maintaining comparable results on
existing single-page benchmarks.