{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T16:10:28Z","timestamp":1775837428354,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,4,29]],"date-time":"2022-04-29T00:00:00Z","timestamp":1651190400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/info13050228","type":"journal-article","created":{"date-parts":[[2022,4,30]],"date-time":"2022-04-30T05:44:07Z","timestamp":1651297447000},"page":"228","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Two New Datasets for Italian-Language Abstractive Text Summarization"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0565-7496","authenticated-orcid":false,"given":"Nicola","family":"Landro","sequence":"first","affiliation":[{"name":"Department of Theoretical and Applied Sciences\u2014DISTA, University of Insubria, Via J.H. Dunant, 3, 21100 Varese, Italy"},{"name":"AIKnowYOU srl, 21029 Varese, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7076-8328","authenticated-orcid":false,"given":"Ignazio","family":"Gallo","sequence":"additional","affiliation":[{"name":"Department of Theoretical and Applied Sciences\u2014DISTA, University of Insubria, Via J.H. Dunant, 3, 21100 Varese, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4355-0366","authenticated-orcid":false,"given":"Riccardo","family":"La Grassa","sequence":"additional","affiliation":[{"name":"INAF-Osservatorio Astronomico di Padova, Vicolo dell\u2019Osservatorio, 35122 Padova, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Edoardo","family":"Federici","sequence":"additional","affiliation":[{"name":"Digitiamo srl, 21100 Varese, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,29]]},"reference":[{"key":"ref_1","first-page":"64","article-title":"A Review on Abstractive Summarization Methods","volume":"59","author":"Khan","year":"2014","journal-title":"J. Theor. Appl. Inf. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wong, K.F., Wu, M., and Li, W. (2008, January 18\u201322). Extractive Summarization Using Supervised and Semi-Supervised Learning. Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), Manchester, UK.","DOI":"10.3115\/1599081.1599205"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Narayan, S., Cohen, S., and Lapata, M. (November, January 31). Don\u2019t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium.","DOI":"10.18653\/v1\/D18-1206"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Nallapati, R., Zhou, B., dos Santos, C., Gul\u00e7ehre, \u00c7., and Xiang, B. (2016, January 11\u201312). Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond. Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning, Berlin, Germany.","DOI":"10.18653\/v1\/K16-1028"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3419106","article-title":"Neural abstractive text summarization with sequence-to-sequence models","volume":"2","author":"Shi","year":"2021","journal-title":"ACM Trans. Data Sci."},{"key":"ref_6","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017, January 4\u20139). Attention is all you need. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_7","first-page":"1","article-title":"Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer","volume":"21","author":"Raffel","year":"2020","journal-title":"J. Mach. Learn. Res."},{"key":"ref_8","unstructured":"Rasley, J., Rajbhandari, S., Ruwase, O., and He, Y. Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters. Proceedings of the Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1017\/S1351324920000601","article-title":"GPT-3: What\u2019s it good for?","volume":"27","author":"Dale","year":"2021","journal-title":"Nat. Lang. Eng."},{"key":"ref_10","unstructured":"Smith, S., Patwary, M., Norick, B., LeGresley, P., Rajbhandari, S., Casper, J., Liu, Z., Prabhumoye, S., Zerveas, G., and Korthikanti, V. (2022). Using deepspeed and megatron to train megatron-turing nlg 530b, a large-scale generative language model. arXiv."},{"key":"ref_11","unstructured":"Sarti, G., and Nissim, M. (2022). IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Xue, L., Constant, N., Roberts, A., Kale, M., Al-Rfou, R., Siddhant, A., Barua, A., and Raffel, C. (2021, January 6\u201311). mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Virtual Event.","DOI":"10.18653\/v1\/2021.naacl-main.41"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"726","DOI":"10.1162\/tacl_a_00343","article-title":"Multilingual denoising pre-training for neural machine translation","volume":"8","author":"Liu","year":"2020","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"ref_14","unstructured":"Mihalcea, R., and Tarau, P. (2004, January 25\u201326). TextRank: Bringing Order into Text. Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, Barcelona, Spain."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.eij.2019.11.001","article-title":"Extractive Arabic text summarization using modified PageRank algorithm","volume":"21","author":"Elbarougy","year":"2020","journal-title":"Egypt. Informatics J."},{"key":"ref_16","unstructured":"Zhang, J., Zhao, Y., Saleh, M., and Liu, P. (2020, January 13\u201318). Pegasus: Pre-training with extracted gap-sentences for abstractive summarization. Proceedings of the International Conference on Machine Learning, PMLR, Virtual Event."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Qi, W., Yan, Y., Gong, Y., Liu, D., Duan, N., Chen, J., Zhang, R., and Zhou, M. (2020, January 16\u201320). ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings, Online.","DOI":"10.18653\/v1\/2020.findings-emnlp.217"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Petroni, F., Piktus, A., Fan, A., Lewis, P., Yazdani, M., Cao, N., Thorne, J., Jernite, Y., Karpukhin, V., and Maillard, J. (2021, January 6\u201311). KILT: A Benchmark for Knowledge Intensive Language Tasks. Proceedings of the NAACL-HLT. Association for Computational Linguistics, Online.","DOI":"10.18653\/v1\/2021.naacl-main.200"},{"key":"ref_19","unstructured":"Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., and Zettlemoyer, L. BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics."},{"key":"ref_20","unstructured":"Tiedemann, J. (2020, January 19\u201320). The Tatoeba Translation Challenge\u2014Realistic Data Sets for Low Resource and Multilingual MT. Proceedings of the Fifth Conference on Machine Translation, Online."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Scialom, T., Dray, P.A., Lamprier, S., Piwowarski, B., and Staiano, J. (2020, January 16\u201320). MLSUM: The Multilingual Summarization Corpus. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), Online.","DOI":"10.18653\/v1\/2020.emnlp-main.647"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., and Funtowicz, M. (2020, January 16\u201320). Transformers: State-of-the-Art Natural Language Processing. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Online.","DOI":"10.18653\/v1\/2020.emnlp-demos.6"},{"key":"ref_23","unstructured":"Lin, C.Y. (2004, January 26). ROUGE: A Package for Automatic Evaluation of Summaries. Proceedings of the Text Summarization Branches Out, Barcelona, Spain."},{"key":"ref_24","unstructured":"Landro, N., Gallo, I., and Federici, E. (2022, April 28). Source code to Create Dataset and Train. Available online: https:\/\/gitlab.com\/nicolalandro\/summarization."},{"key":"ref_25","unstructured":"Landro, N., Gallo, I., and Federici, E. (2021, October 22). It5 Ilpost Trained Model. Available online: https:\/\/huggingface.co\/ARTeLab\/it5-summarization-ilpost."},{"key":"ref_26","unstructured":"Landro, N., Gallo, I., and Federici, E. (2021, October 22). It5 Mlsum Trained Model. Available online: https:\/\/huggingface.co\/ARTeLab\/it5-summarization-mlsum."},{"key":"ref_27","unstructured":"Landro, N., Gallo, I., and Federici, E. (2021, October 22). It5 Fanpage Trained Model. Available online: https:\/\/huggingface.co\/ARTeLab\/it5-summarization-fanpage."},{"key":"ref_28","unstructured":"Landro, N., Gallo, I., and Federici, E. (2021, October 22). Mbart Ilpost Trained Model. Available online: https:\/\/huggingface.co\/ARTeLab\/mbart-summarization-mlsum."},{"key":"ref_29","unstructured":"Landro, N., Gallo, I., and Federici, E. (2021, October 22). Mbart Mlsum Trained Model. Available online: https:\/\/huggingface.co\/ARTeLab\/mbart-summarization-ilpost."},{"key":"ref_30","unstructured":"Landro, N., Gallo, I., and Federici, E. (2021, October 22). Mbart Fanpage Trained Model. Available online: https:\/\/huggingface.co\/ARTeLab\/mbart-summarization-fanpage."},{"key":"ref_31","first-page":"1693","article-title":"Teaching machines to read and comprehend","volume":"28","author":"Hermann","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/13\/5\/228\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:04:04Z","timestamp":1760137444000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/13\/5\/228"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,29]]},"references-count":31,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2022,5]]}},"alternative-id":["info13050228"],"URL":"https:\/\/doi.org\/10.3390\/info13050228","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,29]]}}}