{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/bart-it-an-efficient-sequence-to-sequence","title":"BART-IT: An Efficient Sequence-to-Sequence Model for Italian Text Summarization","arxiv_id":null,"date":"2022-12-27","proceeding":"Future Internet 2022 12","authors":["La Quatra Moreno","Cagliero Luca"],"abstract":"The emergence of attention-based architectures has led to significant improvements in the performance of neural sequence-to-sequence models for text summarization. Although these models have proved to be effective in summarizing English-written documents, their portability to other languages is limited thus leaving plenty of room for improvement. In this paper, we present BART-IT, a sequence-to-sequence model, based on the BART architecture that is specifically tailored to the Italian language. The model is pre-trained on a large corpus of Italian-written pieces of text to learn language-specific features and then fine-tuned on several benchmark datasets established for abstractive summarization. The experimental results show that BART-IT outperforms other state-of-the-art models in terms of ROUGE scores in spite of a significantly smaller number of parameters. The use of BART-IT can foster the development of interesting NLP applications for the Italian language. Beyond releasing the model to the research community to foster further research and applications, we also discuss the ethical implications behind the use of abstractive summarization models.","url_abs":"https://www.mdpi.com/1999-5903/15/1/15","url_pdf":"https://www.mdpi.com/1999-5903/15/1/15/pdf?version=1673418752","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"bart-it-an-efficient-sequence-to-sequence","repo_url":"https://github.com/MorenoLaQuatra/bart-it","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"abstractive-text-summarization","task_name":"Abstractive Text Summarization"},{"task_slug":"text-summarization","task_name":"Text Summarization"}],"methods":[{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bart","method_name":"BART"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/abstractive-text-summarization-on-abstractive","task":"Abstractive Text Summarization","dataset":"Abstractive Text Summarization from Fanpage","model":"mBART","rank_in_archive_order":1,"of":6,"metrics":{"# Parameters":"610","BERTScore":"73.4","ROUGE-1":"36.52","ROUGE-2":"17.52","ROUGE-L":"26.14"},"uses_additional_data":false},{"leaderboard":"/sota/abstractive-text-summarization-on-abstractive","task":"Abstractive Text Summarization","dataset":"Abstractive Text Summarization from Fanpage","model":"BART-IT","rank_in_archive_order":3,"of":6,"metrics":{"# Parameters":"140","BERTScore":"73.24","ROUGE-1":"35.42","ROUGE-2":"15.88","ROUGE-L":"25.12"},"uses_additional_data":false},{"leaderboard":"/sota/abstractive-text-summarization-on-abstractive","task":"Abstractive Text Summarization","dataset":"Abstractive Text Summarization from Fanpage","model":"mT5","rank_in_archive_order":4,"of":6,"metrics":{"# Parameters":"390","BERTScore":"72.77","ROUGE-1":"34.13","ROUGE-2":"15.76","ROUGE-L":"24.84"},"uses_additional_data":false},{"leaderboard":"/sota/abstractive-text-summarization-on-abstractive","task":"Abstractive Text Summarization","dataset":"Abstractive Text Summarization from 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Text Summarization from Il Post","model":"mT5","rank_in_archive_order":4,"of":8,"metrics":{"BERTScore":"74.69","ROUGE-1":"35.04","ROUGE-2":"17.41","ROUGE-L":"28.68"},"uses_additional_data":false},{"leaderboard":"/sota/abstractive-text-summarization-on-abstractive-1","task":"Abstractive Text Summarization","dataset":"Abstractive Text Summarization from Il Post","model":"IT5-base","rank_in_archive_order":6,"of":8,"metrics":{"BERTScore":"71.06","ROUGE-1":"32.88","ROUGE-2":"15.53","ROUGE-L":"26.7"},"uses_additional_data":false},{"leaderboard":"/sota/abstractive-text-summarization-on-wits","task":"Abstractive Text Summarization","dataset":"WITS","model":"BART-IT","rank_in_archive_order":1,"of":4,"metrics":{"BERTScore":"79.28","ROUGE-1":"42.32","ROUGE-2":"28.83","ROUGE-L":"38.84"},"uses_additional_data":false},{"leaderboard":"/sota/abstractive-text-summarization-on-wits","task":"Abstractive Text 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