The Law of Large Documents: Understanding the Structure of Legal Contracts Using Visual Cues
This addresses the challenge of processing long documents in legal applications, though it is incremental as it builds on existing transformer models with added visual features.
The paper tackled the problem of understanding long legal documents by incorporating visual cues like layout and style, achieving improved accuracy on tasks such as segmentation and entity extraction on the Contract Understanding Atticus Dataset.
Large, pre-trained transformer models like BERT have achieved state-of-the-art results on document understanding tasks, but most implementations can only consider 512 tokens at a time. For many real-world applications, documents can be much longer, and the segmentation strategies typically used on longer documents miss out on document structure and contextual information, hurting their results on downstream tasks. In our work on legal agreements, we find that visual cues such as layout, style, and placement of text in a document are strong features that are crucial to achieving an acceptable level of accuracy on long documents. We measure the impact of incorporating such visual cues, obtained via computer vision methods, on the accuracy of document understanding tasks including document segmentation, entity extraction, and attribute classification. Our method of segmenting documents based on structural metadata out-performs existing methods on four long-document understanding tasks as measured on the Contract Understanding Atticus Dataset.