Improving abstractive summarization of legal rulings through textual entailment

• Beltagy I, Peters ME, Cohan A (2020) Longformer: The long-document transformer arXiv preprint arXiv:200405150

• Cao Z, Wei F, Li W, Li S (2018) Faithful to the original: fact aware neural abstractive summarization. In: thirty-second AAAI conference on artificial intelligence

• Carbonell J, Goldstein J (1998) The use of mmr, diversity-based reranking for reordering documents and producing summaries. In: proceedings of the 21st annual international ACM SIGIR conference on research and development in information retrieval, pp 335–336

• Chandrasekaran MK, Yasunaga M, Radev D, Freitag D, Kan MY (2019) Overview and results: Cl-scisumm shared task 2019. In: in proceedings of joint workshop on bibliometric-enhanced information retrieval and NLP for digital libraries (BIRNDL 2019)

• Child R, Gray S, Radford A, Sutskever I (2019) Generating long sequences with sparse transformers. arXiv preprint arXiv:190410509

• Compton P, Jansen R (1990) Knowledge in context: A strategy for expert system maintenance. In: proceedings of the second Australian joint conference on artificial intelligence, Springer-Verlag, Berlin, Heidelberg, AI ’88, p 292–306

• Devlin J, Chang MW, Lee K, Toutanova K (2018) BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:181004805

• Falke T, Ribeiro LF, Utama PA, Dagan I, Gurevych I (2019) Ranking generated summaries by correctness: an interesting but challenging application for natural language inference. In: proceedings of the 57th annual meeting of the association for computational linguistics, pp 2214–2220

• Fan A, Grangier D, Auli M (2018) Controllable abstractive summarization. In: proceedings of the 2nd workshop on neural machine translation and generation, 45–54

• Feijo D, Moreira V (2018) Rulingbr: A summarization dataset for legal texts. In: computational processing of the portuguese language (PROPOR 2018), Springer International Publishing, pp 255–264

• Feijo D, Moreira V (2019) Summarizing legal rulings: comparative experiments. in: proceedings of the international conference on recent advances in natural language processing (RANLP 2019), pp 313–322

• 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 115–123

• Gelbart D, Smith J (1991) Beyond boolean search: Flexicon, a legal text-based intelligent system. In: proceedings of the 3rd international conference on Artificial intelligence and law, pp 225–234

• Goldhan D, Eckart T, Quasthoff U (2012) Building large monolingual dictionaries at the leipzig corpora collection: From 100 to 200 languages. In: proceedings of the 8th international language resources and evaluation (LREC’12)

• Goodrich B, Rao V, Liu PJ, Saleh M (2019) Assessing the factual accuracy of generated text. In: proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pp 166–175

• Grover C, Hachey B, Hughson I, Korycinski C (2003a) Automatic summarisation of legal documents. In: proceedings of the 9th international conference on artificial intelligence and law, association for computing machinery, ICAIL ’03, p 243–251

• Grover C, Hachey B, Korycinski C (2003b). Summarising legal texts: Sentential tense and argumentative roles. In: proceedings of the HLT-NAACL 03 on text summarization workshop-volume 5, association for computational linguistics, pp 33–40

• Guimarães JAC (2011) Elaboração de ementas jurisprudenciais: elementos teórico-metodológicos. Série Monografias do CEJ 9

• Kitaev N, Kaiser Ł, Levskaya A (2020) Reformer: the efficient transformer arXiv preprint arXiv:200104451

• Klein G, Kim Y, Deng Y, Senellart J, Rush A (2017). OpenNMT: Open-source toolkit for neural machine translation. In: proceedings of ACL 2017, system demonstrations, association for computational linguistics, Vancouver, Canada, pp 67–72

• Kryściński W, McCann B, Xiong C, Socher R (2020) Evaluating the factual consistency of abstractive text summarization. In: proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP), pp 9332–9346

• 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: proceedings of the 58th annual meeting of the association for computational linguistics, association for computational linguistics, Online, pp 7871–7880, https://doi.org/10.18653/v1/2020.acl-main.703, https://aclanthology.org/2020.acl-main.703

• Lin CY (2004) Rouge: a package for automatic evaluation of summaries. In: text summarization branches out, pp 74–81

• Liu Y (2019) Fine-tune BERT for extractive summarization. arXiv preprint arXiv:190310318

• Liu Y, Lapata M (2019) Fine-tune BERT for extractive summarization. arXiv preprint arXiv:190310318

• Luijtgaarden N (2019) Automatic summarization of legal text. Utrecht University Master’s thesis

• Matsumaru K, Takase S, Okazaki N (2020) Text summarization with pretrained encoders. In: proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), pp 3721–3731

• Maynez J, Narayan S, Bohnet B, McDonald R (2020) On faithfulness and factuality in abstractive summarization. In: proceedings of the 58th annual meeting of the association for computational linguistics, pp 1906–1919

• Moens MF, Uyttendaele C (1997) Automatic text structuring and categorization as a first step in summarizing legal cases. Inf Proces Manag 33(6):727–737

  Article  Google Scholar 

• Mudrakarta PK, Taly A, Sundararajan M, Dhamdhere K (2018) Did the model understand the question? In: proceedings of the 56th annual meeting of the association for computational linguistics (Volume 1: Long Papers), pp 1896–1906

• Pandya V (2019) Automatic text summarization of legal cases: A hybrid approach. 5th international conference on advances in computer science and information technology (ACSTY-2019)

• Paulus R, Xiong C, Socher R (2017) A deep reinforced model for abstractive summarization. arXiv preprint arXiv:170504304

• Pires T, Schlinger E, Garrette D (2019) How multilingual is multilingual BERT? In: proceedings of the 57th annual meeting of the association for computational linguistics, pp 4996–5001

• Roy A, Saffar M, Vaswani A, Grangier D (2021) Efficient content-based sparse attention with routing transformers. Transac Assoc Comput Linguistics 9:53–68

  Article  Google Scholar 

• See A, Liu PJ, Manning CD (2017) Get to the point: Summarization with pointer-generator networks. In: proceedings of the 55th annual meeting of the association for computational linguistics 1:1073–1083

• Tay Y, Bahri D, Yang L, Metzler D, Juan DC (2020) Sparse sinkhorn attention. In: international conference on machine learning, PMLR, pp 9438–9447

• Turtle H (1995) Text retrieval in the legal world. Artif Intell and Law 3(1):5–54

  Article  Google Scholar 

• Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. In: proceedings of the 31st international conference on neural information processing systems, pp 6000–6010

• Wolf T, Debut L, Sanh V, Chaumond J, Delangue C, Moi A, Cistac P, Rault T, Louf R, Funtowicz M, et al. (2019) Huggingface’s transformers: state-of-the-art natural language processing. arXiv preprint arXiv:191003771

• Yousfi-Monod M, Farzindar A, Lapalme G (2010) Supervised machine learning for summarizing legal documents. In: Canadian conference on artificial intelligence, Springer, pp 51–62

• Zhang J, Zhao Y, Saleh M, Liu P (2020) Pegasus: Pre-training with extracted gap-sentences for abstractive summarization. In: international conference on machine learning, PMLR, pp 11328–11339

• Zhang X, Wei F, Zhou M (2019) HIBERT: Document level pre-training of hierarchical bidirectional transformers for document summarization. In: proceedings of the 57th annual meeting of the association for computational linguistics, pp 5059–5069

• Zhao Z, Cohen SB, Webber B (2020) Reducing quantity hallucinations in abstractive summarization. arXiv preprint arXiv:200913312

• Zhong L, Zhong Z, Zhao Z, Wang S, Ashley KD, Grabmair M (2019) Automatic summarization of legal decisions using iterative masking of predictive sentences. In: proceedings of the seventeenth international conference on artificial intelligence and law, ICAIL ’19, p 163–172

Download references