Efficient Classification of Long Documents Using Transformers

Understand

Several methods have been proposed for classifying long textual documents using Transformers.

• However, there is a lack of consensus on a benchmark to enable a fair comparison among different approaches.
• In this paper, we provide a comprehensive evaluation of the relative efficacy measured against various baselines and diverse datasets -- both in terms of accuracy as well as time and space overheads.
• Our datasets cover binary, multi-class, and multi-label classification tasks and represent various ways information is organized in a long text (e.g.