@inproceedings{garimella-etal-2022-text,
title = "Text Simplification for Legal Domain: {{I}}nsights and Challenges",
author = "Garimella, Aparna and
Sancheti, Abhilasha and
Aggarwal, Vinay and
Ganesh, Ananya and
Chhaya, Niyati and
Kambhatla, Nandakishore",
editor = "Aletras, Nikolaos and
Chalkidis, Ilias and
Barrett, Leslie and
Goan{\textcommabelow{t}}{\u{a}}, C{\u{a}}t{\u{a}}lina and
Preo{\textcommabelow{t}}iuc-Pietro, Daniel",
booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.nllp-1.28",
doi = "10.18653/v1/2022.nllp-1.28",
pages = "296--304",
abstract = "Legal documents such as contracts contain complex and domain-specific jargons, long and nested sentences, and often present with several details that may be difficult to understand for laypeople without domain expertise. In this paper, we explore the problem of text simplification (TS) in legal domain. The main challenge to this is the lack of availability of complex-simple parallel datasets for the legal domain. We investigate some of the existing datasets, methods, and metrics in the TS literature for simplifying legal texts, and perform human evaluation to analyze the gaps. We present some of the challenges involved, and outline a few open questions that need to be addressed for future research in this direction.",
}
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<abstract>Legal documents such as contracts contain complex and domain-specific jargons, long and nested sentences, and often present with several details that may be difficult to understand for laypeople without domain expertise. In this paper, we explore the problem of text simplification (TS) in legal domain. The main challenge to this is the lack of availability of complex-simple parallel datasets for the legal domain. We investigate some of the existing datasets, methods, and metrics in the TS literature for simplifying legal texts, and perform human evaluation to analyze the gaps. We present some of the challenges involved, and outline a few open questions that need to be addressed for future research in this direction.</abstract>
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%0 Conference Proceedings
%T Text Simplification for Legal Domain: Insights and Challenges
%A Garimella, Aparna
%A Sancheti, Abhilasha
%A Aggarwal, Vinay
%A Ganesh, Ananya
%A Chhaya, Niyati
%A Kambhatla, Nandakishore
%Y Aletras, Nikolaos
%Y Chalkidis, Ilias
%Y Barrett, Leslie
%Y Goan\textcommabelowtă, Cătălina
%Y Preo\textcommabelowtiuc-Pietro, Daniel
%S Proceedings of the Natural Legal Language Processing Workshop 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F garimella-etal-2022-text
%X Legal documents such as contracts contain complex and domain-specific jargons, long and nested sentences, and often present with several details that may be difficult to understand for laypeople without domain expertise. In this paper, we explore the problem of text simplification (TS) in legal domain. The main challenge to this is the lack of availability of complex-simple parallel datasets for the legal domain. We investigate some of the existing datasets, methods, and metrics in the TS literature for simplifying legal texts, and perform human evaluation to analyze the gaps. We present some of the challenges involved, and outline a few open questions that need to be addressed for future research in this direction.
%R 10.18653/v1/2022.nllp-1.28
%U https://aclanthology.org/2022.nllp-1.28
%U https://doi.org/10.18653/v1/2022.nllp-1.28
%P 296-304
Markdown (Informal)
[Text Simplification for Legal Domain: {I}nsights and Challenges](https://aclanthology.org/2022.nllp-1.28) (Garimella et al., NLLP 2022)
ACL
- Aparna Garimella, Abhilasha Sancheti, Vinay Aggarwal, Ananya Ganesh, Niyati Chhaya, and Nandakishore Kambhatla. 2022. Text Simplification for Legal Domain: {I}nsights and Challenges. In Proceedings of the Natural Legal Language Processing Workshop 2022, pages 296–304, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.