Towards Argument-Aware Abstractive Summarization of Long Legal Opinions
  with Summary Reranking
Mohamed Elaraby Yang Zhong Diane Litman
We propose a simple approach for the abstractive summarization of long legal
opinions that considers 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.