  Given the number of Arabic speakers worldwide and the notably large amount of
content in the web today in some fields such as law, medicine, or even news,
documents of considerable length are produced regularly. Classifying those
documents using traditional learning models is often impractical since extended
length of the documents increases computational requirements to an
unsustainable level. Thus, it is necessary to customize these models
specifically for long textual documents. In this paper we propose two simple
but effective models to classify long length Arabic documents. We also
fine-tune two different models-namely, Longformer and RoBERT, for the same task
and compare their results to our models. Both of our models outperform the
Longformer and RoBERT in this task over two different datasets.
