Context-Aware Classification of Legal Document Pages
This addresses a practical need for businesses processing legal documents by enabling more accurate page classification, though it is incremental as it builds on existing context-aware techniques.
The paper tackled the problem of classifying pages in legal documents by incorporating context from neighboring pages, overcoming input length limitations of pre-trained models, and achieved significant performance improvements on English and Portuguese datasets compared to non-recurrent and other context-aware methods.
For many business applications that require the processing, indexing, and retrieval of professional documents such as legal briefs (in PDF format etc.), it is often essential to classify the pages of any given document into their corresponding types beforehand. Most existing studies in the field of document image classification either focus on single-page documents or treat multiple pages in a document independently. Although in recent years a few techniques have been proposed to exploit the context information from neighboring pages to enhance document page classification, they typically cannot be utilized with large pre-trained language models due to the constraint on input length. In this paper, we present a simple but effective approach that overcomes the above limitation. Specifically, we enhance the input with extra tokens carrying sequential information about previous pages - introducing recurrence - which enables the usage of pre-trained Transformer models like BERT for context-aware page classification. Our experiments conducted on two legal datasets in English and Portuguese respectively show that the proposed approach can significantly improve the performance of document page classification compared to the non-recurrent setup as well as the other context-aware baselines.