BART-IT: An Efficient Sequence-to-Sequence Model for Italian Text Summarization

The emergence of attention-based architectures has led to significant improvements in the performance neural sequence-to-sequence models for text summarization. Although these have proved be effective summarizing English-written documents, their portability other languages is limited thus leaving plenty room improvement. In this paper, we present BART-IT, a model, based on BART architecture that specifically tailored Italian language. model pre-trained large corpus Italian-written pieces learn language-specific features and then fine-tuned several benchmark datasets established abstractive experimental results show BART-IT outperforms state-of-the-art terms ROUGE scores spite significantly smaller number parameters. use can foster development interesting NLP applications Beyond releasing research community further applications, also discuss ethical implications behind summarization models.