STRONG -- Structure Controllable Legal Opinion Summary Generation
Yang Zhong Diane Litman
We propose an approach for the structure controllable summarization of long
legal opinions that considers the argument structure of the document. Our
approach involves using predicted argument role information to guide the model
in generating coherent summaries that follow a provided structure pattern. We
demonstrate the effectiveness of our approach on a dataset of legal opinions
and show that it outperforms several strong baselines with respect to ROUGE,
BERTScore, and structure similarity.