Efficient Classification of Long Documents Using Transformers
This work addresses the need for robust models in long document classification for researchers and practitioners, but it is incremental as it focuses on benchmarking rather than introducing new methods.
The paper tackled the problem of classifying long documents using Transformers by evaluating various methods against baselines across diverse datasets, finding that complex models often fail to outperform simple baselines and show inconsistent performance.
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. information that is critical to making the classification decision is at the beginning or towards the end of the document). Our results show that more complex models often fail to outperform simple baselines and yield inconsistent performance across datasets. These findings emphasize the need for future studies to consider comprehensive baselines and datasets that better represent the task of long document classification to develop robust models.