{"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/two-new-datasets-for-italian-language","title":"Two New Datasets for Italian-Language Abstractive Text Summarization","arxiv_id":null,"date":"2022-04-29","proceeding":"Information 2022 4","authors":["Nicola Landro","Ignazio Gallo","Riccardo La Grassa","Edoardo Federici"],"abstract":"Text summarization aims to produce a short summary containing relevant parts from a given text. Due to the lack of data for abstractive summarization on low-resource languages such as Italian, we propose two new original datasets collected from two Italian news websites with multi-sentence summaries and corresponding articles, and from a dataset obtained by machine translation of a Spanish summarization dataset. These two datasets are currently the only two available in Italian for this task. To evaluate the quality of these two datasets, we used them to train a T5-base model and an mBART model, obtaining good results with both. To better evaluate the results obtained, we also compared the same models trained on automatically translated datasets, and the resulting summaries in the same training language, with the automatically translated summaries, which demonstrated the superiority of the models obtained from the proposed datasets.","url_abs":"https://www.mdpi.com/2078-2489/13/5/228","url_pdf":"https://mdpi-res.com/d_attachment/information/information-13-00228/article_deploy/information-13-00228.pdf?version=1651225131","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":"two-new-datasets-for-italian-language","repo_url":"https://gitlab.com/nicolalandro/summarization","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"abstractive-text-summarization","task_name":"Abstractive Text Summarization"},{"task_slug":"articles","task_name":"Articles"},{"task_slug":"machine-translation","task_name":"Machine Translation"},{"task_slug":"sentence","task_name":"Sentence"},{"task_slug":"text-summarization","task_name":"Text Summarization"},{"task_slug":"translation","task_name":"Translation"},{"task_slug":"two","task_name":"Vocal Bursts Valence Prediction"}],"methods":[{"method_slug":"adafactor","method_name":"Adafactor"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"attention-dropout","method_name":"Attention Dropout"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"glu","method_name":"Gated Linear Unit"},{"method_slug":"inverse-square-root-schedule","method_name":"Inverse Square Root Schedule"},{"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":"sentencepiece","method_name":"SentencePiece"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"t5","method_name":"T5"},{"method_slug":"mbart","method_name":"mBART"}],"datasets_introduced":[{"slug":"abstractive-text-summarization-from-fanpage","name":"Abstractive Text Summarization from Fanpage","full_name":""},{"slug":"abstractive-text-summarization-from-il-post","name":"Abstractive Text Summarization from Il Post","full_name":""},{"slug":"mlsum-it","name":"MLSum-it","full_name":""}],"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":2,"of":6,"metrics":{"ROUGE-1":"36.50"},"uses_additional_data":false},{"leaderboard":"/sota/abstractive-text-summarization-on-abstractive","task":"Abstractive Text Summarization","dataset":"Abstractive Text Summarization from Fanpage","model":"IT5","rank_in_archive_order":6,"of":6,"metrics":{"ROUGE-1":"33.83"},"uses_additional_data":false},{"leaderboard":"/sota/abstractive-text-summarization-on-abstractive-1","task":"Abstractive Text Summarization","dataset":"Abstractive Text Summarization from Il Post","model":"mBART","rank_in_archive_order":2,"of":8,"metrics":{"ROUGE-1":"38.91"},"uses_additional_data":false},{"leaderboard":"/sota/abstractive-text-summarization-on-abstractive-1","task":"Abstractive Text Summarization","dataset":"Abstractive Text Summarization from Il 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Summarization","dataset":"MLSum-it","model":"mBART","rank_in_archive_order":1,"of":4,"metrics":{"rouge1":"19.35"},"uses_additional_data":false},{"leaderboard":"/sota/abstractive-text-summarization-on-mlsum-it","task":"Abstractive Text Summarization","dataset":"MLSum-it","model":"IT5","rank_in_archive_order":2,"of":4,"metrics":{"rouge1":"19.29"},"uses_additional_data":false},{"leaderboard":"/sota/abstractive-text-summarization-on-mlsum-it","task":"Abstractive Text Summarization","dataset":"MLSum-it","model":"Pegasus-CNN/DM (eng-it translation)","rank_in_archive_order":3,"of":4,"metrics":{"rouge1":"16.97"},"uses_additional_data":false},{"leaderboard":"/sota/abstractive-text-summarization-on-mlsum-it","task":"Abstractive Text Summarization","dataset":"MLSum-it","model":"Pegasus-XSum (eng-it 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