  Legal professionals face the challenge of managing an overwhelming volume of
lengthy judgments, making automated legal case summarization crucial. However,
prior approaches mainly focused on training and evaluating these models within
the same jurisdiction. In this study, we explore the cross-jurisdictional
generalizability of legal case summarization models.Specifically, we explore
how to effectively summarize legal cases of a target jurisdiction where
reference summaries are not available. In particular, we investigate whether
supplementing models with unlabeled target jurisdiction corpus and extractive
silver summaries obtained from unsupervised algorithms on target data enhances
transfer performance. Our comprehensive study on three datasets from different
jurisdictions highlights the role of pre-training in improving transfer
performance. We shed light on the pivotal influence of jurisdictional
similarity in selecting optimal source datasets for effective transfer.
Furthermore, our findings underscore that incorporating unlabeled target data
yields improvements in general pre-trained models, with additional gains when
silver summaries are introduced. This augmentation is especially valuable when
dealing with extractive datasets and scenarios featuring limited alignment
between source and target jurisdictions. Our study provides key insights for
developing adaptable legal case summarization systems, transcending
jurisdictional boundaries.
