Towards Argument-Aware Abstractive Summarization of Long Legal Opinions with Summary Reranking

Understand

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.