Leveraging ParsBERT and Pretrained mT5 for Persian Abstractive Text Summarization

• URL: http://arxiv.org/abs/2012.11204v1
• Date: Mon, 21 Dec 2020 09:35:52 GMT
• Title: Leveraging ParsBERT and Pretrained mT5 for Persian Abstractive Text Summarization
• Authors: Mehrdad Farahani, Mohammad Gharachorloo, Mohammad Manthouri
• Abstract summary: This paper 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.
• Score: 1.0742675209112622
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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