  This paper presents an attempt to build a Modern Standard Arabic (MSA)
sentence-level simplification system. We experimented with sentence
simplification using two approaches: (i) a classification approach leading to
lexical simplification pipelines which use Arabic-BERT, a pre-trained
contextualised model, as well as a model of fastText word embeddings; and (ii)
a generative approach, a Seq2Seq technique by applying a multilingual
Text-to-Text Transfer Transformer mT5. We developed our training corpus by
aligning the original and simplified sentences from the internationally
acclaimed Arabic novel "Saaq al-Bambuu". We evaluate effectiveness of these
methods by comparing the generated simple sentences to the target simple
sentences using the BERTScore evaluation metric. The simple sentences produced
by the mT5 model achieve P 0.72, R 0.68 and F-1 0.70 via BERTScore, while,
combining Arabic-BERT and fastText achieves P 0.97, R 0.97 and F-1 0.97. In
addition, we report a manual error analysis for these experiments.
\url{https://github.com/Nouran-Khallaf/Lexical_Simplification}
