{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T14:09:09Z","timestamp":1777990149861,"version":"3.51.4"},"reference-count":77,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T00:00:00Z","timestamp":1695600000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T00:00:00Z","timestamp":1695600000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Alma Mater Studiorum - Universit\u00e0 di Bologna"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Law"],"published-print":{"date-parts":[[2024,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Analyzing and evaluating legal case reports are labor-intensive tasks for judges and lawyers, who usually base their decisions on report abstracts, legal principles, and commonsense reasoning. Thus, summarizing legal documents is time-consuming and requires excellent human expertise. Moreover, public legal corpora of specific languages are almost unavailable. This paper proposes a transfer learning approach with extractive and abstractive techniques to cope with the lack of labeled legal summarization datasets, namely a low-resource scenario. In particular, we conducted extensive multi- and cross-language experiments. The proposed work outperforms the state-of-the-art results of extractive summarization on the Australian Legal Case Reports dataset and sets a new baseline for abstractive summarization. Finally, syntactic and semantic metrics assessments have been carried out to evaluate the accuracy and the factual consistency of the machine-generated legal summaries.<\/jats:p>","DOI":"10.1007\/s10506-023-09373-8","type":"journal-article","created":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T20:28:06Z","timestamp":1695673686000},"page":"1111-1139","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Multi-language transfer learning for low-resource legal case summarization"],"prefix":"10.1007","volume":"32","author":[{"given":"Gianluca","family":"Moro","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicola","family":"Piscaglia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3574-9962","authenticated-orcid":false,"given":"Luca","family":"Ragazzi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Paolo","family":"Italiani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,25]]},"reference":[{"key":"9373_CR1","doi-asserted-by":"publisher","unstructured":"Akbik A, Bergmann T, Blythe D, Rasul K, Schweter S, Vollgraf R (2019) FLAIR: an easy-to-use framework for state-of-the-art NLP. In: Ammar W, Louis A, Mostafazadeh N (eds) Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2\u20137, 2019, Demonstrations. Association for Computational Linguistics, pp 54\u201359. https:\/\/doi.org\/10.18653\/v1\/n19-4010","DOI":"10.18653\/v1\/n19-4010"},{"key":"9373_CR2","doi-asserted-by":"crossref","unstructured":"Bae S, Kim T, Kim J, Lee S (2019) Summary level training of sentence rewriting for abstractive summarization. arXiv:1909.08752","DOI":"10.18653\/v1\/D19-5402"},{"key":"9373_CR3","unstructured":"Bahdanau D, Cho K, Bengio Y (2015) Neural machine translation by jointly learning to align and translate. In: Bengio Y, LeCun Y (eds) 3rd International conference on learning representations, ICLR 2015, San Diego, CA, USA, May 7\u20139, 2015, Conference track proceedings. http:\/\/arxiv.org\/abs\/1409.0473"},{"key":"9373_CR4","doi-asserted-by":"publisher","unstructured":"Bajaj A, Dangati P, Krishna K, Kumar PA, Uppaal R, Windsor B, Brenner E, Dotterrer D, Das R, McCallum (2021) A Long document summarization in a low resource setting using pretrained language models. In: Kabbara J, Lin H, Paullada A, Vamvas J (eds) Proceedings of the ACL-IJCNLP 2021 student research workshop. ACL, pp 71\u201380. https:\/\/doi.org\/10.18653\/v1\/2021.acl-srw.7","DOI":"10.18653\/v1\/2021.acl-srw.7"},{"key":"9373_CR5","unstructured":"Beltagy I, Peters ME, Cohan A (2020) Longformer: the long-document transformer. arXiv:2004.05150"},{"key":"9373_CR6","unstructured":"Bhargava R, Nigwekar S, Sharma Y (2017) Catchphrase extraction from legal documents using LSTM networks. In: Majumder P, Mitra M, Mehta P, Sankhavara J (eds) Working Notes of FIRE 2017\u2014proceedings of forum for information retrieval evaluation, Bangalore, India, December 8\u201310, 2017. CEUR Workshop , vol 2036, pp 72\u201373. http:\/\/ceur-ws.org\/Vol-2036\/T3-3.pdf"},{"key":"9373_CR7","first-page":"101","volume":"137","author":"W Cerroni","year":"2013","unstructured":"Cerroni W, Moro G, Pirini T, Ramilli M (2013) Peer-to-peer data mining classifiers for decentralized detection of network attacks. In Proceedings of the Twenty-Fourth Australasian Database Conference 137:101\u2013107","journal-title":"In Proceedings of the Twenty-Fourth Australasian Database Conference"},{"key":"9373_CR8","doi-asserted-by":"crossref","unstructured":"Cerroni W, Moro G, Pasolini R, Ramilli M (2015) Decentralized detection of network attacks through P2P data clustering of SNMP data. Computers & Security 52:1\u201316","DOI":"10.1016\/j.cose.2015.03.006"},{"key":"9373_CR9","doi-asserted-by":"publisher","unstructured":"Cho K, van Merrienboer B, G\u00fcl\u00e7ehre \u00c7, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using RNN encoder-decoder for statistical machine translation. In: Moschitti A, Pang B, Daelemans W (eds) Proceedings of the 2014 conference on empirical methods in natural language processing, EMNLP 2014, October 25\u201329, 2014, Doha, Qatar, a meeting of SIGDAT, a Special Interest Group of The ACL, pp 1724\u20131734. https:\/\/doi.org\/10.3115\/v1\/d14-1179","DOI":"10.3115\/v1\/d14-1179"},{"key":"9373_CR10","doi-asserted-by":"publisher","unstructured":"Cohan A, Dernoncourt F, Kim DS, Bui T, Kim S, Chang W, Goharian N (2018) A discourse-aware attention model for abstractive summarization of long documents. In: Walker MA, Ji H, Stent A (eds) Proceedings of the 2018 conference of the North American chapter of the association for computational linguistics: human language technologies, NAACL-HLT, New Orleans, Louisiana, USA, June 1\u20136, 2018, vol 2 (Short Papers) Association for computational linguistics, pp 615\u2013621. https:\/\/doi.org\/10.18653\/v1\/n18-2097","DOI":"10.18653\/v1\/n18-2097"},{"key":"9373_CR11","doi-asserted-by":"publisher","unstructured":"Devlin J, Chang M, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: Burstein J, Doran C, Solorio T (eds) Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2\u20137, 2019, Volume 1 (Long and Short Papers). Association for Computational Linguistics, pp 4171\u20134186. https:\/\/doi.org\/10.18653\/v1\/n19-1423","DOI":"10.18653\/v1\/n19-1423"},{"key":"9373_CR12","doi-asserted-by":"publisher","unstructured":"Dong Y, Shen Y, Crawford E, van Hoof H, Cheung JCK (2018) Banditsum: extractive summarization as a contextual bandit. In: Riloff E, Chiang D, Hockenmaier J, Tsujii J (eds) Proceedings of the 2018 conference on empirical methods in natural language processing, Brussels, Belgium, October 31\u2013November 4, 2018. Association for Computational Linguistics, pp 3739\u20133748. https:\/\/doi.org\/10.18653\/v1\/d18-1409","DOI":"10.18653\/v1\/d18-1409"},{"key":"9373_CR13","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.cmpb.2015.12.002","volume":"126","author":"G Domeniconi","year":"2016","unstructured":"Domeniconi G, Masseroli M, Moro G, Pinoli P (2016) Cross-organism learning method to discover new gene functionalities. In Computer methods and programs in biomedicine 126:20\u201334","journal-title":"In Computer methods and programs in biomedicine"},{"key":"9373_CR14","doi-asserted-by":"crossref","unstructured":"Domeniconi G, Moro G, Pagliarani A, Pasolini, R (2015) Markov chain based method for in-domain and cross-domain sentiment classification. In 2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K) IEEE 1:127\u2013137","DOI":"10.5220\/0005636001270137"},{"key":"9373_CR15","doi-asserted-by":"crossref","unstructured":"Domeniconi G, Moro G, Pagliarani A, Pasolini R (2017) On Deep Learning in Cross-Domain Sentiment Classification. In KDIR pp 50\u201360","DOI":"10.5220\/0006488100500060"},{"key":"9373_CR16","doi-asserted-by":"crossref","unstructured":"Domeniconi, G, Moro, G, Pasolini, R, Sartori, C (2014) In Cross-domain Text Classification through Iterative Refining of Target Categories Representations. In KDIR pp 31\u201342","DOI":"10.5220\/0005069400310042"},{"key":"9373_CR17","doi-asserted-by":"publisher","unstructured":"Feng X, Feng X, Qin B (2022) MSAMSum: Towards benchmarking multi-lingual dialogue summarization. In: Proceedings of the second DialDoc workshop on document-grounded dialogue and conversational question answering. Association for Computational Linguistics, Dublin, Ireland, pp 1\u201312. https:\/\/doi.org\/10.18653\/v1\/2022.dialdoc-1.1","DOI":"10.18653\/v1\/2022.dialdoc-1.1"},{"key":"9373_CR18","doi-asserted-by":"crossref","unstructured":"Frisoni G, Moro G (2021) Phenomena explanation from text: Unsupervised learning of interpretable and statistically significant knowledge. In Data Management Technologies and Applications: 9th International Conference, DATA 2020, Virtual Event, July 7\u20139, 2020, Revised Selected Papers 9 (pp. 293-318). Springer International Publishing.","DOI":"10.1007\/978-3-030-83014-4_14"},{"key":"9373_CR19","doi-asserted-by":"crossref","unstructured":"Frisoni G, Moro G, Carbonaro A (2020) Learning Interpretable and Statistically Significant Knowledge from Unlabeled Corpora of Social Text Messages: A Novel Methodology of Descriptive Text Mining. In DATA pp 121\u2013132","DOI":"10.5220\/0009892001210132"},{"key":"9373_CR20","doi-asserted-by":"publisher","first-page":"160721","DOI":"10.1109\/ACCESS.2021.3130956","volume":"9","author":"G Frisoni","year":"2021","unstructured":"Frisoni G, Moro G, Carbonaro A (2021) A survey on event extraction for natural language understanding: Riding the biomedical literature wave. IEEE Access 9:160721\u2013160757","journal-title":"IEEE Access"},{"key":"9373_CR21","doi-asserted-by":"crossref","unstructured":"Frisoni G, Italiani P, Salvatori S, Moro G (2023) Cogito ergo summ: abstractive summarization of biomedical papers via semantic parsing graphs and consistency rewards. In: Proceedings of the AAAI Conference on Artificial Intelligence 37(11) 12781-12789","DOI":"10.1609\/aaai.v37i11.26503"},{"key":"9373_CR22","doi-asserted-by":"crossref","unstructured":"Frisoni G, Mizutani M, Moro G, Valgimigli L (2022) Bioreader: a retrieval-enhanced text-to-text transformer for biomedical literature. In: Proceedings of the 2022 conference on empirical methods in natural language processing pp 5770\u20135793","DOI":"10.18653\/v1\/2022.emnlp-main.390"},{"key":"9373_CR23","unstructured":"Galgani F, Compton P, Hoffmann A (2012) Combining different summarization techniques for legal text. In: Proceedings of the workshop on innovative hybrid approaches to the processing of textual data. Association for Computational Linguistics, pp 115\u2013123"},{"key":"9373_CR24","doi-asserted-by":"publisher","unstructured":"Galgani F, Compton P, Hoffmann AG (2012) Citation based summarisation of legal texts. In: Anthony P, Ishizuka M, Lukose D (eds) PRICAI 2012: trends in artificial intelligence\u2013proceedings of 12th Pacific Rim international conference on artificial intelligence, Kuching, Malaysia, September 3\u20137, 2012. Lecture Notes in Computer Science, vol. 7458. Springer, pp 40\u201352. https:\/\/doi.org\/10.1007\/978-3-642-32695-0_6","DOI":"10.1007\/978-3-642-32695-0_6"},{"key":"9373_CR25","doi-asserted-by":"publisher","unstructured":"Galgani F, Compton P, Hoffmann AG (2012) Knowledge acquisition for categorization of legal case reports. In: Richards D, Kang BH (eds) Knowledge management and acquisition for intelligent systems\u201312th Pacific Rim knowledge acquisition workshop, PKAW 2012, Kuching, Malaysia, September 5\u20136, 2012. Lecture Notes in Computer Science, vol 7457. Springer, pp 118\u2013132. https:\/\/doi.org\/10.1007\/978-3-642-32541-0_10","DOI":"10.1007\/978-3-642-32541-0_10"},{"key":"9373_CR26","doi-asserted-by":"publisher","unstructured":"Galgani F, Compton P, Hoffmann AG (2012) Towards automatic generation of catchphrases for legal case reports. In: Gelbukh AF (ed) Computational linguistics and intelligent text processing\u201313th international conference, CICLing 2012, New Delhi, India, March 11\u201317, 2012, Part II. Lecture notes in computer science, vol 7182. Springer, pp 414\u2013425. https:\/\/doi.org\/10.1007\/978-3-642-28601-8_35","DOI":"10.1007\/978-3-642-28601-8_35"},{"key":"9373_CR27","doi-asserted-by":"publisher","unstructured":"Galgani F, Hoffmann AG (2010) LEXA: towards automatic legal citation classification. In: Li J (ed) Proceedings of AI 2010: advances in artificial intelligence\u201323rd Australasian joint conference, Adelaide, Australia, December 7\u201310, 2010. Lecture Notes in Computer Science, vol 6464. Springer, pp 445\u2013454. https:\/\/doi.org\/10.1007\/978-3-642-17432-2_45","DOI":"10.1007\/978-3-642-17432-2_45"},{"key":"9373_CR28","doi-asserted-by":"publisher","unstructured":"Grusky M, Naaman M, Artzi Y (2018) Newsroom: a dataset of 1.3 million summaries with diverse extractive strategies. In: Walker MA, Ji H, Stent A (eds) Proceedings of the 2018 conference of the North American chapter of the association for computational linguistics: human language technologies, NAACL-HLT 2018, New Orleans, Louisiana, USA, June 1\u20136, 2018, Volume 1 (Long Papers). Association for Computational Linguistics, pp 708\u2013719. https:\/\/doi.org\/10.18653\/v1\/n18-1065","DOI":"10.18653\/v1\/n18-1065"},{"key":"9373_CR29","doi-asserted-by":"publisher","unstructured":"Gu N, Ash E, Hahnloser R (2022) MemSum: extractive summarization of long documents using multi-step episodic Markov decision processes. In: Proceedings of the 60th annual meeting of the association for computational linguistics (volume 1: Long Papers). Association for Computational Linguistics, Dublin, Ireland, pp 6507\u20136522. https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.450","DOI":"10.18653\/v1\/2022.acl-long.450"},{"key":"9373_CR30","doi-asserted-by":"publisher","unstructured":"Guo H, Pasunuru R, Bansal M (2018) Soft layer-specific multi-task summarization with entailment and question generation. In: Gurevych I, Miyao Y (eds) Proceedings of the 56th annual meeting of the association for computational linguistics, ACL 2018, Melbourne, Australia, July 15\u201320, 2018, Volume 1: Long Papers. Association for Computational Linguistics, pp 687\u2013697. https:\/\/doi.org\/10.18653\/v1\/P18-1064","DOI":"10.18653\/v1\/P18-1064"},{"key":"9373_CR31","doi-asserted-by":"publisher","unstructured":"Huang L, Cao S, Parulian N.N, Ji H, Wang L (2021) Efficient attentions for long document summarization. In: Toutanova K, Rumshisky A, Zettlemoyer L, Hakkani-T\u00fcr D, Beltagy I, Bethard S, Cotterell R, Chakraborty T, Zhou Y (eds) Proceedings of the 2021 conference of the North American chapter of the association for computational linguistics: human language technologies, NAACL-HLT 2021, Online, June 6\u201311, 2021. Association for Computational Linguistics, pp 1419\u20131436. https:\/\/doi.org\/10.18653\/v1\/2021.naacl-main.112","DOI":"10.18653\/v1\/2021.naacl-main.112"},{"key":"9373_CR32","doi-asserted-by":"crossref","unstructured":"Kayalvizhi S, Thenmozhi D (2020) Deep learning approach for extracting catch phrases from legal documents. In: Neural networks for natural language processing. IGI Global, pp 143\u2013158","DOI":"10.4018\/978-1-7998-1159-6.ch009"},{"key":"9373_CR33","unstructured":"Koboyatshwene T, Lefoane M, Narasimhan L (2017) Machine learning approaches for catchphrase extraction in legal documents. In: Majumder P, Mitra M, Mehta P, Sankhavara J (eds) Working Notes of FIRE 2017\u2013forum for information retrieval evaluation, Bangalore, India, December 8\u201310, 2017. CEUR workshop proceedings, vol 2036, pp 95\u201398. http:\/\/ceur-ws.org\/Vol-2036\/T3-11.pdf"},{"key":"9373_CR34","doi-asserted-by":"crossref","unstructured":"Kornilova A, Eidelman V (2019) Billsum: a corpus for automatic summarization of US legislation. arXiv:1910.00523","DOI":"10.18653\/v1\/D19-5406"},{"key":"9373_CR35","doi-asserted-by":"publisher","unstructured":"Kryscinski W, McCann B, Xiong C, Socher R (2020) Evaluating the factual consistency of abstractive text summarization. In: Webber B, Cohn T, He Y, Liu Y (eds) Proceedings of the 2020 conference on empirical methods in natural language processing, EMNLP 2020, November 16\u201320, 2020. Association for Computational Linguistics, pp 9332\u20139346. https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.750","DOI":"10.18653\/v1\/2020.emnlp-main.750"},{"key":"9373_CR36","doi-asserted-by":"publisher","unstructured":"Kryscinski W, Paulus R, Xiong C, Socher R (2018) Improving abstraction in text summarization. In: Riloff E, Chiang D, Hockenmaier J, Tsujii J (eds) Proceedings of the 2018 conference on empirical methods in natural language processing, Brussels, Belgium, October 31\u2013November 4, 2018. Association for Computational Linguistics, pp 1808\u20131817. https:\/\/doi.org\/10.18653\/v1\/d18-1207","DOI":"10.18653\/v1\/d18-1207"},{"key":"9373_CR37","doi-asserted-by":"publisher","unstructured":"LeCun Y, Haffner P, Bottou L, Bengio Y (1999) Object recognition with gradient-based learning. In: Forsyth DA, Mundy JL, Ges\u00f9 VD, Cipolla R (eds) Shape, contour and grouping in computer vision. Lecture Notes in Computer Science, vol 1681. Springer. https:\/\/doi.org\/10.1007\/3-540-46805-6_19","DOI":"10.1007\/3-540-46805-6_19"},{"key":"9373_CR38","doi-asserted-by":"publisher","unstructured":"Lewis M, Liu Y, Goyal N, Ghazvininejad M, Mohamed A, Levy O, Stoyanov V, Zettlemoyer L (2020) BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In: Jurafsky D, Chai J, Schluter N, Tetreault JR (eds) Proceedings of the 58th annual meeting of the association for computational linguistics, ACL 2020, Online, July 5\u201310, 2020. Association for Computational Linguistics, pp 7871\u20137880. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.703","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"9373_CR39","unstructured":"Lin C-Y (2004) ROUGE: a package for automatic evaluation of summaries. In: Text summarization branches out. Association for Computational Linguistics, Barcelona, Spain, pp 74\u201381. https:\/\/www.aclweb.org\/anthology\/W04-1013"},{"key":"9373_CR40","unstructured":"Liu Y (2019) Fine-tune BERT for extractive summarization. arXiv:1903.10318"},{"key":"9373_CR41","doi-asserted-by":"publisher","unstructured":"Liu Y, Lapata M (2019) Text summarization with pretrained encoders. In: Inui K, Jiang J, Ng V, Wan X (eds) Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing, EMNLP-IJCNLP 2019, Hong Kong, China, November 3\u20137, 2019. Association for Computational Linguistics, pp 3728\u20133738. https:\/\/doi.org\/10.18653\/v1\/D19-1387","DOI":"10.18653\/v1\/D19-1387"},{"key":"9373_CR42","unstructured":"Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, Levy O, Lewis M, Zettlemoyer L, Stoyanov V (2019) Roberta: a robustly optimized BERT pretraining approach. arXiv:1907.11692"},{"key":"9373_CR43","first-page":"171","volume":"104","author":"S Lodi","year":"2010","unstructured":"Lodi S, Moro G, Sartori C (2010) Distributed data clustering in multi-dimensional peer-to-peer networks. In Proceedings of the Twenty-First Australasian Conference on Database Technologies 104:171\u2013178","journal-title":"In Proceedings of the Twenty-First Australasian Conference on Database Technologies"},{"key":"9373_CR44","doi-asserted-by":"publisher","unstructured":"Mandal A, Ghosh K, Pal A, Ghosh S (2017) Automatic catchphrase identification from legal court case documents. In: Lim E, Winslett M, Sanderson M, Fu AW, Sun J, Culpepper JS, Lo E, Ho JC, Donato D, Agrawal R, Zheng Y, Castillo C, Sun A, Tseng VS, Li C (eds) Proceedings of the 2017 ACM on conference on information and knowledge management, CIKM 2017, Singapore, November 06\u201310, 2017. ACM, pp 3728\u20133738. https:\/\/doi.org\/10.1145\/3132847.3133102","DOI":"10.1145\/3132847.3133102"},{"key":"9373_CR45","unstructured":"McCann B, Bradbury J, Xiong C, Socher R (2017) Learned in translation: Contextualized word vectors. In: Guyon I, von Luxburg U, Bengio S, Wallach HM, Fergus R, Vishwanathan SVN, Garnett R (eds) Advances in neural information processing systems 30: annual conference on neural information processing systems 2017, December 4-9, Long Beach, CA, USA, pp 6294\u20136305. https:\/\/proceedings.neurips.cc\/paper\/2017\/hash\/20c86a628232a67e7bd46f76fba7ce12-Abstract.html"},{"key":"9373_CR46","unstructured":"Mihalcea R, Tarau P (2004) Textrank: Bringing order into text. In: Proceedings of the 2004 conference on empirical methods in natural language processing , EMNLP 2004, a meeting of SIGDAT, a Special Interest Group of the ACL, held in conjunction with ACL 2004, 25\u201326 July 2004, Barcelona, Spain. ACL, pp 404\u2013411. https:\/\/www.aclweb.org\/anthology\/W04-3252\/"},{"issue":"4","key":"9373_CR47","doi-asserted-by":"publisher","first-page":"1218","DOI":"10.1016\/j.jnca.2011.05.002","volume":"35","author":"G Moro","year":"2012","unstructured":"Moro G, Monti G (2012) W-Grid: A scalable and efficient self-organizing infrastructure for multi-dimensional data management, querying and routing in wireless data-centric sensor networks. In Journal of Network and Computer Applications 35(4):1218\u20131234","journal-title":"In Journal of Network and Computer Applications"},{"key":"9373_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2023.126356","volume":"545","author":"G Moro","year":"2023","unstructured":"Moro G, Ragazzi L (2023) Align-then-abstract representation learning for low-resource summarization. Neurocomputing 545:126356","journal-title":"Neurocomputing"},{"key":"9373_CR49","doi-asserted-by":"publisher","DOI":"10.3390\/s23073542","author":"G Moro","year":"2023","unstructured":"Moro G, Ragazzi L, Valgimigli L, Frisoni G, Sartori C, Marfia G (2023) Efficient memory-enhanced transformer for long-document summarization in low-resource regimes. Sensors. https:\/\/doi.org\/10.3390\/s23073542","journal-title":"Sensors"},{"key":"9373_CR50","doi-asserted-by":"crossref","unstructured":"Moro G, Ragazzi L (2022) Semantic self-segmentation for abstractive summarization of long documents in low-resource regimes. In: AAAI 2022, virtual event, February 22 - March 1, 2022. AAAI Press, pp 11085\u201311093","DOI":"10.1609\/aaai.v36i10.21357"},{"key":"9373_CR51","doi-asserted-by":"crossref","unstructured":"Moro G, Ragazzi L, Valgimigli L (2023) Carburacy: summarization models tuning and comparison in eco-sustainable regimes with a novel carbon-aware accuracy. In: Proceedings of the AAAI Conference on Artificial Intelligence 2023, Washington, DC, USA, February 7\u201314, 2023. AAAI Press, 37(12):14417\u201314425","DOI":"10.1609\/aaai.v37i12.26686"},{"key":"9373_CR52","doi-asserted-by":"publisher","unstructured":"Moro G, Ragazzi L, Valgimigli L, Freddi D (2022) Discriminative marginalized probabilistic neural method for multi-document summarization of medical literature. ACL (Volume 1: Long Papers). ACL, Dublin, pp 180\u2013189. https:\/\/doi.org\/10.18653\/v1\/2022.acl-long.15","DOI":"10.18653\/v1\/2022.acl-long.15"},{"key":"9373_CR53","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1007\/978-3-031-17849-8_4","volume-title":"International Conference on Similarity Search and Applications","author":"G Moro","year":"2022","unstructured":"Moro G, Salvatori S (2022) Deep Vision-Language Model for Efficient Multi-modal Similarity Search in Fashion Retrieval. International Conference on Similarity Search and Applications. Springer International Publishing, Cham, pp 40\u201353"},{"key":"9373_CR54","doi-asserted-by":"crossref","unstructured":"Moro G, Salvatori S, Frisoni G (2023) Efficient text-image semantic search: A multi-modal vision-language approach for fashion retrieval. In: Neurocomputing, 538, 126196.","DOI":"10.1016\/j.neucom.2023.03.057"},{"key":"9373_CR55","doi-asserted-by":"publisher","unstructured":"Moro G, Valgimigli L (2021) Efficient self-supervised metric information retrieval: A bibliography based method applied to COVID literature. In: Sensors 21(19):6430. https:\/\/doi.org\/10.3390\/s21196430","DOI":"10.3390\/s21196430"},{"key":"9373_CR56","doi-asserted-by":"publisher","unstructured":"Nallapati R, Zhou B, dos Santos CN, G\u00fcl\u00e7ehre \u00c7, Xiang B (2016) Abstractive text summarization using sequence-to-sequence rnns and beyond. In: Goldberg Y, Riezler S (eds.) Proceedings of the 20th SIGNLL conference on computational natural language learning, CoNLL 2016, Berlin, Germany, August 11\u201312, 2016. ACL, pp 280\u2013290. https:\/\/doi.org\/10.18653\/v1\/k16-1028","DOI":"10.18653\/v1\/k16-1028"},{"key":"9373_CR57","doi-asserted-by":"publisher","unstructured":"Narayan S, Cohen SB, Lapata M (2018) Don\u2019t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization. In: Riloff E, Chiang D, Hockenmaier J, Tsujii J (eds) Proceedings of the 2018 conference on empirical methods in natural language processing, Brussels, Belgium, October 31\u2013November 4, 2018. Association for Computational Linguistics, pp 1797\u20131807. https:\/\/doi.org\/10.18653\/v1\/d18-1206","DOI":"10.18653\/v1\/d18-1206"},{"key":"9373_CR58","doi-asserted-by":"publisher","unstructured":"Pasunuru R, Bansal M (2018) Multi-reward reinforced summarization with saliency and entailment. In: Walker MA, Ji H, Stent A (eds) Proceedings of the 2018 conference of the North American chapter of the association for computational linguistics: human language technologies, NAACL-HLT, New Orleans, Louisiana, USA, June 1-6, 2018, Volume 2 (Short Papers). Association for Computational Linguistics, pp 646\u2013653. https:\/\/doi.org\/10.18653\/v1\/n18-2102","DOI":"10.18653\/v1\/n18-2102"},{"key":"9373_CR59","doi-asserted-by":"publisher","unstructured":"Peters ME, Neumann M, Iyyer M, Gardner M, Clark C, Lee K, Zettlemoyer L (2018) Deep contextualized word representations. In: Walker MA, Ji H, Stent A (eds) Proceedings of the 2018 conference of the North American chapter of the association for computational linguistics: human language technologies, NAACL-HLT 2018, New Orleans, Louisiana, USA, June 1\u20136, 2018, vol 1 (Long Papers). Association for Computational Linguistics, pp 2227\u20132237. https:\/\/doi.org\/10.18653\/v1\/n18-1202","DOI":"10.18653\/v1\/n18-1202"},{"key":"9373_CR60","doi-asserted-by":"publisher","unstructured":"Pilault J, Li R, Subramanian S, Pal C (2020) On extractive and abstractive neural document summarization with transformer language models. In: Webber B, Cohn T, He Y, Liu Y (eds) Proceedings of the 2020 conference on empirical methods in natural language processing, EMNLP 2020, Online, November 16\u201320. Association for Computational Linguistics, pp 9308\u20139319. https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.748","DOI":"10.18653\/v1\/2020.emnlp-main.748"},{"key":"9373_CR61","doi-asserted-by":"publisher","unstructured":"Qi W, Yan Y, Gong Y, Liu D, Duan N, Chen J, Zhang R, Zhou M (2020) Prophetnet: predicting future n-gram for sequence-to-sequence pre-training. In: Cohn T, He Y, Liu Y (eds) Proceedings of the 2020 conference on empirical methods in natural language processing: findings, EMNLP 2020, Online Event, 16-20 November 2020. Findings of ACL, vol EMNLP 2020. Association for Computational Linguistics, pp 2401\u20132410. https:\/\/doi.org\/10.18653\/v1\/2020.findings-emnlp.217","DOI":"10.18653\/v1\/2020.findings-emnlp.217"},{"key":"9373_CR62","unstructured":"Radford A, Wu J, Child R, Luan D, Amodei D, Sutskever I (2019) Language models are unsupervised multitask learners. OpenAI Blog 1(8)"},{"issue":"140","key":"9373_CR63","first-page":"1","volume":"21","author":"C Raffel","year":"2020","unstructured":"Raffel C, Shazeer N, Roberts A, Lee K, Narang S, Matena M, Zhou Y, Li W, Liu PJ (2020) Exploring the limits of transfer learning with a unified text-to-text transformer. J Mach Learn Res 21(140):1\u201367","journal-title":"J Mach Learn Res"},{"key":"9373_CR64","unstructured":"Sandhaus E (2008) The New York times annotated corpus. Linguistic Data Consortium, Philadelphia 6(12):26752"},{"key":"9373_CR65","unstructured":"Sanh V, Debut L, Chaumond J, Wolf T (2019) Distilbert, a distilled version of BERT: smaller, faster, cheaper and lighter. arXiv:1910.01108"},{"key":"9373_CR66","doi-asserted-by":"publisher","unstructured":"See A, Liu PJ, Manning CD (2017) Get to the point: Summarization with pointer-generator networks. In: Barzilay R, Kan M (eds) Proceedings of the 55th annual meeting of the association for computational linguistics, ACL 2017, Vancouver, Canada, July 30\u2013August 4, vol 1: Long Papers. Association for Computational Linguistics, pp 1073\u20131083. https:\/\/doi.org\/10.18653\/v1\/P17-1099","DOI":"10.18653\/v1\/P17-1099"},{"key":"9373_CR67","doi-asserted-by":"publisher","unstructured":"Sharma E, Li C, Wang L (2019) BIGPATENT: a large-scale dataset for abstractive and coherent summarization. In: Korhonen A, Traum DR, M\u00e0rquez L (eds) Proceedings of the 57th conference of the association for computational linguistics, ACL 2019, Florence, Italy, July 28- August 2, 2019, vol 1: Long Papers. Association for Computational Linguistics, pp 2204\u20132213. https:\/\/doi.org\/10.18653\/v1\/p19-1212","DOI":"10.18653\/v1\/p19-1212"},{"key":"9373_CR68","unstructured":"Shukla A, Bhattacharya P, Poddar S, Mukherjee R, Ghosh K, Goyal P, Ghosh S (2022) Legal case document summarization: extractive and abstractive methods and their evaluation. In: He Y, Ji H, Liu Y, Li S, Chang C, Poria S, Lin C, Buntine WL, Liakata M, Yan H, Yan Z, Ruder S, Wan X, Arana-Catania M, Wei Z, Huang H, Wu J, Day M, Liu P, Xu R (eds) Proceedings of the 2nd conference of the Asia-Pacific chapter of the association for computational linguistics and the 12th international joint conference on natural language processing, AACL\/IJCNLP 2022\u2013vol 1: Long Papers, Online Only, November 20\u201323, 2022. Association for Computational Linguistics, pp 1048\u20131064. https:\/\/aclanthology.org\/2022.aacl-main.77"},{"key":"9373_CR69","unstructured":"Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In: Ghahramani Z, Welling M, Cortes C, Lawrence ND, Weinberger KQ (eds) Advances in neural information processing systems 27: annual conference on neural information processing systems 2014, December 8-13 2014, Montreal, Quebec, Canada, pp 3104\u20133112. https:\/\/proceedings.neurips.cc\/paper\/2014\/hash\/a14ac55a4f27472c5d894ec1c3c743d2-Abstract.html"},{"key":"9373_CR70","unstructured":"Tran VD, Nguyen ML, Satoh K (2018) Automatic catchphrase extraction from legal case documents via scoring using deep neural networks. arXiv:1809.05219"},{"key":"9373_CR71","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. In: Guyon I, von Luxburg U, Bengio S, Wallach HM, Fergus R, Vishwanathan SVN, Garnett R (eds) Advances in neural information processing systems 30: annual conference on neural information processing systems 2017, December 4\u20139, 2017, Long Beach, CA, USA, pp 5998\u20136008. https:\/\/proceedings.neurips.cc\/paper\/2017\/hash\/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html"},{"key":"9373_CR72","unstructured":"Vinyals O, Fortunato M, Jaitly N (2015) Pointer networks. In: Cortes C, Lawrence ND, Lee DD, Sugiyama M, Garnett R (eds) Advances in neural information processing systems 28: annual conference on neural information processing systems 2015, December 7-12, 2015, Montreal, Quebec, Canada, pp 2692\u20132700. https:\/\/proceedings.neurips.cc\/paper\/2015\/hash\/29921001f2f04bd3baee84a12e98098f-Abstract.html"},{"key":"9373_CR73","doi-asserted-by":"crossref","unstructured":"Wu Y, Hu B (2018) Learning to extract coherent summary via deep reinforcement learning. In: McIlraith SA, Weinberger KQ (eds) Proceedings of the thirty-second aaAI conference on artificial intelligence, (AAAI-18), the 30th innovative applications of artificial intelligence (IAAI-18), and the 8th AAAI symposium on educational advances in artificial intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018. AAAI Press, pp 5602\u20135609. https:\/\/www.aaai.org\/ocs\/index.php\/AAAI\/AAAI18\/paper\/view\/16838","DOI":"10.1609\/aaai.v32i1.11987"},{"key":"9373_CR74","unstructured":"Zaheer M, Guruganesh G, Dubey K.A, Ainslie J, Alberti C, Onta\u00f1\u00f3n S, Pham P, Ravula A, Wang Q, Yang L, Ahmed A (2020) Big bird: Transformers for longer sequences. In: Larochelle H, Ranzato M, Hadsell R, Balcan M, Lin H (eds) Advances in neural information processing systems 33: annual conference on neural information processing systems 2020, NeurIPS 2020, December 6-12, 2020, Virtual. https:\/\/proceedings.neurips.cc\/paper\/2020\/hash\/c8512d142a2d849725f31a9a7a361ab9-Abstract.html"},{"key":"9373_CR75","doi-asserted-by":"publisher","unstructured":"Zhang H, Cai J, Xu J, Wang J (2019) Pretraining-based natural language generation for text summarization. In: Bansal M, Villavicencio A (eds) Proceedings of the 23rd conference on computational natural language learning, CoNLL 2019, Hong Kong, China, November 3-4, 2019. Association for Computational Linguistics, pp 789\u2013797. https:\/\/doi.org\/10.18653\/v1\/K19-1074","DOI":"10.18653\/v1\/K19-1074"},{"key":"9373_CR76","unstructured":"Zhang J, Zhao Y, Saleh M, Liu PJ (2020) PEGASUS: pre-training with extracted gap-sentences for abstractive summarization. In: Proceedings of the 37th international conference on machine learning, ICML 2020, 13\u201318 July 2020, Virtual Event. Proceedings of machine learning research, vol 119. PMLR, pp 11328\u201311339. http:\/\/proceedings.mlr.press\/v119\/zhang20ae.html"},{"key":"9373_CR77","doi-asserted-by":"publisher","unstructured":"Zhong M, Liu P, Wang D, Qiu X, Huang X (2019) Searching for effective neural extractive summarization: What works and what\u2019s next. In: Korhonen A, Traum DR, M\u00e0rquez L (eds) Proceedings of the 57th conference of the association for computational linguistics, ACL 2019, Florence, Italy, July 28- August 2, 2019, vol 1: Long Papers. Association for Computational Linguistics, pp 1049\u20131058. https:\/\/doi.org\/10.18653\/v1\/p19-1100","DOI":"10.18653\/v1\/p19-1100"}],"container-title":["Artificial Intelligence and Law"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10506-023-09373-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10506-023-09373-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10506-023-09373-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,23]],"date-time":"2024-10-23T12:12:53Z","timestamp":1729685573000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10506-023-09373-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,25]]},"references-count":77,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,12]]}},"alternative-id":["9373"],"URL":"https:\/\/doi.org\/10.1007\/s10506-023-09373-8","relation":{},"ISSN":["0924-8463","1572-8382"],"issn-type":[{"value":"0924-8463","type":"print"},{"value":"1572-8382","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,25]]},"assertion":[{"value":"5 June 2023","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 September 2023","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}