@inproceedings{shukla-etal-2022-legal,
title = "Legal Case Document Summarization: Extractive and Abstractive Methods and their Evaluation",
author = "Shukla, Abhay and
Bhattacharya, Paheli and
Poddar, Soham and
Mukherjee, Rajdeep and
Ghosh, Kripabandhu and
Goyal, Pawan and
Ghosh, Saptarshi",
editor = "He, Yulan and
Ji, Heng and
Li, Sujian and
Liu, Yang and
Chang, Chua-Hui",
booktitle = "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 (Volume 1: Long Papers)",
month = nov,
year = "2022",
address = "Online only",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.aacl-main.77/",
doi = "10.18653/v1/2022.aacl-main.77",
pages = "1048--1064",
abstract = "Summarization of legal case judgement documents is a challenging problem in Legal NLP. However, not much analyses exist on how different families of summarization models (e.g., extractive vs. abstractive) perform when applied to legal case documents. This question is particularly important since many recent transformer-based abstractive summarization models have restrictions on the number of input tokens, and legal documents are known to be very long. Also, it is an open question on how best to evaluate legal case document summarization systems. In this paper, we carry out extensive experiments with several extractive and abstractive summarization methods (both supervised and unsupervised) over three legal summarization datasets that we have developed. Our analyses, that includes evaluation by law practitioners, lead to several interesting insights on legal summarization in specific and long document summarization in general."
}
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%0 Conference Proceedings
%T Legal Case Document Summarization: Extractive and Abstractive Methods and their Evaluation
%A Shukla, Abhay
%A Bhattacharya, Paheli
%A Poddar, Soham
%A Mukherjee, Rajdeep
%A Ghosh, Kripabandhu
%A Goyal, Pawan
%A Ghosh, Saptarshi
%Y He, Yulan
%Y Ji, Heng
%Y Li, Sujian
%Y Liu, Yang
%Y Chang, Chua-Hui
%S 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 (Volume 1: Long Papers)
%D 2022
%8 November
%I Association for Computational Linguistics
%C Online only
%F shukla-etal-2022-legal
%X Summarization of legal case judgement documents is a challenging problem in Legal NLP. However, not much analyses exist on how different families of summarization models (e.g., extractive vs. abstractive) perform when applied to legal case documents. This question is particularly important since many recent transformer-based abstractive summarization models have restrictions on the number of input tokens, and legal documents are known to be very long. Also, it is an open question on how best to evaluate legal case document summarization systems. In this paper, we carry out extensive experiments with several extractive and abstractive summarization methods (both supervised and unsupervised) over three legal summarization datasets that we have developed. Our analyses, that includes evaluation by law practitioners, lead to several interesting insights on legal summarization in specific and long document summarization in general.
%R 10.18653/v1/2022.aacl-main.77
%U https://aclanthology.org/2022.aacl-main.77/
%U https://doi.org/10.18653/v1/2022.aacl-main.77
%P 1048-1064
Markdown (Informal)
[Legal Case Document Summarization: Extractive and Abstractive Methods and their Evaluation](https://aclanthology.org/2022.aacl-main.77/) (Shukla et al., AACL-IJCNLP 2022)
ACL
- Abhay Shukla, Paheli Bhattacharya, Soham Poddar, Rajdeep Mukherjee, Kripabandhu Ghosh, Pawan Goyal, and Saptarshi Ghosh. 2022. Legal Case Document Summarization: Extractive and Abstractive Methods and their Evaluation. In 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 (Volume 1: Long Papers), pages 1048–1064, Online only. Association for Computational Linguistics.