@inproceedings{elaraby-etal-2023-towards,
title = "Towards Argument-Aware Abstractive Summarization of Long Legal Opinions with Summary Reranking",
author = "Elaraby, Mohamed and
Zhong, Yang and
Litman, Diane",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.481",
doi = "10.18653/v1/2023.findings-acl.481",
pages = "7601--7612",
abstract = "We propose a simple approach for the abstractive summarization of long legal opinions that takes into account the argument structure of the document. Legal opinions often contain complex and nuanced argumentation, making it challenging to generate a concise summary that accurately captures the main points of the legal opinion. Our approach involves using argument role information to generate multiple candidate summaries, then reranking these candidates based on alignment with the document{'}s argument structure. We demonstrate the effectiveness of our approach on a dataset of long legal opinions and show that it outperforms several strong baselines.",
}
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<abstract>We propose a simple approach for the abstractive summarization of long legal opinions that takes into account the argument structure of the document. Legal opinions often contain complex and nuanced argumentation, making it challenging to generate a concise summary that accurately captures the main points of the legal opinion. Our approach involves using argument role information to generate multiple candidate summaries, then reranking these candidates based on alignment with the document’s argument structure. We demonstrate the effectiveness of our approach on a dataset of long legal opinions and show that it outperforms several strong baselines.</abstract>
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%0 Conference Proceedings
%T Towards Argument-Aware Abstractive Summarization of Long Legal Opinions with Summary Reranking
%A Elaraby, Mohamed
%A Zhong, Yang
%A Litman, Diane
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F elaraby-etal-2023-towards
%X We propose a simple approach for the abstractive summarization of long legal opinions that takes into account the argument structure of the document. Legal opinions often contain complex and nuanced argumentation, making it challenging to generate a concise summary that accurately captures the main points of the legal opinion. Our approach involves using argument role information to generate multiple candidate summaries, then reranking these candidates based on alignment with the document’s argument structure. We demonstrate the effectiveness of our approach on a dataset of long legal opinions and show that it outperforms several strong baselines.
%R 10.18653/v1/2023.findings-acl.481
%U https://aclanthology.org/2023.findings-acl.481
%U https://doi.org/10.18653/v1/2023.findings-acl.481
%P 7601-7612
Markdown (Informal)
[Towards Argument-Aware Abstractive Summarization of Long Legal Opinions with Summary Reranking](https://aclanthology.org/2023.findings-acl.481) (Elaraby et al., Findings 2023)
ACL