@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.",
}

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.
Elaraby, Mohamed Zhong, Yang Litman, Diane
Towards Argument-Aware Abstractive Summarization of Long Legal Opinions with Summary Reranking