@article{VALENTE19991079,
title = {Legal modeling and automated reasoning with ON-LINE},
journal = {International Journal of Human-Computer Studies},
volume = {51},
number = {6},
pages = {1079-1125},
year = {1999},
issn = {1071-5819},
doi = {https://doi.org/10.1006/ijhc.1999.0298},
url = {https://www.sciencedirect.com/science/article/pii/S1071581999902987},
author = {ANDRÉ VALENTE and JOOST BREUKER and BOB BROUWER},
abstract = {In this paper we present a modeling approach to legal knowledge systems and its computational realization in the ON-LINE architecture. ON-LINE has modules for modeling legal sources, for storing and retrieving legal information and for reasoning with legal knowledge. The approach takes two perspectives: domain and task. In the domain perspective, a core ontology divides legal knowledge into five major categories: normative, world, responsibility, reactive and creative. For the normative knowledge, which is most typical of legal domains, we developed a new representation and inference formalisms which are an alternative to deontic logic. For the world knowledge, we argue for using a terminological knowledge representation language. The structure of the ontology is not a taxonomy, but a network of dependencies between the categories. These dependencies reflect the global structure of arguments in legal reasoning. In the task perspective, we followed a top-down approach using the CommonKADS modeling library. Design, planning and assessment were identified as typical tasks in the legal domain. For assessment, a model was specified and implemented.}
}
@article{liu2019roberta,
  title={Roberta: A robustly optimized bert pretraining approach},
  author={Liu, Yinhan and Ott, Myle and Goyal, Naman and Du, Jingfei and Joshi, Mandar and Chen, Danqi and Levy, Omer and Lewis, Mike and Zettlemoyer, Luke and Stoyanov, Veselin},
  journal={arXiv preprint arXiv:1907.11692},
  year={2019}
}
@article{he2020deberta,
  title={Deberta: Decoding-enhanced bert with disentangled attention},
  author={He, Pengcheng and Liu, Xiaodong and Gao, Jianfeng and Chen, Weizhu},
  journal={arXiv preprint arXiv:2006.03654},
  year={2020}
}
@article{devlin2018bert,
  title={Bert: Pre-training of deep bidirectional transformers for language understanding},
  author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
  journal={arXiv preprint arXiv:1810.04805},
  year={2018}
}
@article{wu2021r,
  title={R-drop: Regularized dropout for neural networks},
  author={Wu, Lijun and Li, Juntao and Wang, Yue and Meng, Qi and Qin, Tao and Chen, Wei and Zhang, Min and Liu, Tie-Yan and others},
  journal={Advances in Neural Information Processing Systems},
  volume={34},
  pages={10890--10905},
  year={2021}
}
@article{zhang2021personalized,
  title={Personalized sentiment classification of customer reviews via an interactive attributes attention model},
  author={Zhang, You and Wang, Jin and Zhang, Xuejie},
  journal={Knowledge-Based Systems},
  volume={226},
  pages={107135},
  year={2021},
  publisher={Elsevier}
}
@article{KONG2022107872,
title = {Hierarchical BERT with an adaptive fine-tuning strategy for document classification},
journal = {Knowledge-Based Systems},
volume = {238},
pages = {107872},
year = {2022},
issn = {0950-7051},
doi = {https://doi.org/10.1016/j.knosys.2021.107872},
url = {https://www.sciencedirect.com/science/article/pii/S0950705121010479},
author = {Jun Kong and Jin Wang and Xuejie Zhang},
keywords = {Document classification, Hierarchical BERT, Adaptive fine-tuning strategy, Pretrained language model},
abstract = {Pretrained language models (PLMs) have achieved impressive results and have become vital tools for various natural language processing (NLP) tasks. However, there is a limitation that applying these PLMs to document classification when the document length exceeds the maximum acceptable length of the PLM since the excess portion is truncated in these models. If the keywords are in the truncated part, then the performance of the model declines. To address this problem, this paper proposes a hierarchical BERT with an adaptive fine-tuning strategy (HAdaBERT). It consists of a BERT-based model as the local encoder and an attention-based gated memory network as the global encoder. In contrast to existing PLMs that directly truncate documents, the proposed model uses a part of the document as a region, dividing input document into several containers. This allows the useful information in each container to be extracted by a local encoder and composed by a global encoder according to its contribution to the classification. To further improve the performance of the model, this paper proposes an adaptive fine-tuning strategy, which dynamically decides the layers of BERT to be fine-tuned instead of fine-tuning all layers for each input text. Experimental results on different corpora indicated that this method outperformed existing neural networks for document classification.}
}
@article{beltagy2020longformer,
  title={Longformer: The long-document transformer},
  author={Beltagy, Iz and Peters, Matthew E and Cohan, Arman},
  journal={arXiv preprint arXiv:2004.05150},
  year={2020}
}
@article{zaheer2020big,
  title={Big bird: Transformers for longer sequences},
  author={Zaheer, Manzil and Guruganesh, Guru and Dubey, Kumar Avinava and Ainslie, Joshua and Alberti, Chris and Ontanon, Santiago and Pham, Philip and Ravula, Anirudh and Wang, Qifan and Yang, Li and others},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={17283--17297},
  year={2020}
}
@article{vaswani2017attention,
  title={Attention is all you need},
  author={Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, {\L}ukasz and Polosukhin, Illia},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}
@inproceedings{koay-etal-2021-sliding,
    title = "A Sliding-Window Approach to Automatic Creation of Meeting Minutes",
    author = "Koay, Jia Jin  and
      Roustai, Alexander  and
      Dai, Xiaojin  and
      Liu, Fei",
    editor = "Durmus, Esin  and
      Gupta, Vivek  and
      Liu, Nelson  and
      Peng, Nanyun  and
      Su, Yu",
    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.naacl-srw.10",
    doi = "10.18653/v1/2021.naacl-srw.10",
    pages = "68--75",
    abstract = "Meeting minutes record any subject matter discussed, decisions reached and actions taken at the meeting. The importance of automatic minuting cannot be overstated. In this paper, we present a sliding window approach to automatic generation of meeting minutes. It aims at addressing issues pertaining to the nature of spoken text, including the lengthy transcript and lack of document structure, which make it difficult to identify salient content to be included in meeting minutes. Our approach combines a sliding-window approach and a neural abstractive summarizer to navigate through the raw transcript to find salient content. The approach is evaluated on transcripts of natural meeting conversations, where we compare results obtained for human transcripts and two versions of automatic transcripts and discuss how and to what extent the summarizer succeeds at capturing salient content.",
}
