Towards Argument-Aware Abstractive Summarization of Long Legal Opinions with Summary Reranking
This addresses the challenge of summarizing complex legal documents for legal professionals, though it appears incremental as it builds on existing summarization methods with argument-aware refinements.
The authors tackled abstractive summarization of long legal opinions by incorporating argument structure, generating multiple candidate summaries and reranking them based on alignment with argument roles. Their approach outperformed strong baselines on a dataset of long legal opinions.
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.