Summarisation of German Judgments in conjunction with a Class-based Evaluation
This work addresses the need for automated summarization to aid legal experts, though it is incremental as it builds on existing methods with domain-specific enhancements.
The authors tackled the problem of automatically summarizing German legal judgments by fine-tuning a decoder-based large language model enriched with legal entities, but found that the quality of the summaries was insufficient for practical use.
The automated summarisation of long legal documents can be a great aid for legal experts in their daily work. We automatically create summaries (guiding principles) of German judgments by fine-tuning a decoder-based large language model. We enrich the judgments with information about legal entities before the training. For the evaluation of the created summaries, we define a set of evaluation classes which allows us to measure their language, pertinence, completeness and correctness. Our results show that employing legal entities helps the generative model to find the relevant content, but the quality of the created summaries is not yet sufficient for a use in practice.