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arXiv 2012.112042020-12-21cs.CL

Leveraging ParsBERT and Pretrained mT5 for Persian Abstractive Text Summarization

Islamic Azad University North Tehran Branch（伊斯兰阿扎德大学北德黑兰分校）；Queensland University of Technology（昆士兰科技大学）

Mehrdad Farahani, Mohammad Gharachorloo, Mohammad Manthouri

备注：: 7 pages, 7 figures, 3 tables, csicc2021 conference
DOI：: 10.1109/CSICC52343.2021.9420563

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英文摘要：

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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