  Text summarization is one of the most critical Natural Language Processing
(NLP) tasks. More and more researches are conducted in this field every day.
Pre-trained transformer-based encoder-decoder models have begun to gain
popularity for these tasks. This paper proposes two methods to address this
task and introduces a novel dataset named pn-summary for Persian abstractive
text summarization. The models employed in this paper are mT5 and an
encoder-decoder version of the ParsBERT model (i.e., a monolingual BERT model
for Persian). These models are fine-tuned on the pn-summary dataset. The
current work is the first of its kind and, by achieving promising results, can
serve as a baseline for any future work.
