Summarisation of German Judgments in conjunction with a Class-based Evaluation
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 11 of its 38 references that are in the map. First posted on arXiv 9 May 2025.
Builds on
11 of 38 references.
References · 11
- 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
- 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,355
- 2023FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generationcited by 1,259
- 2021BARTScore: Evaluating Generated Text as Text Generationcited by 719
- 2016SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documentscited by 245
- 2020A Dataset of German Legal Documents for Named Entity Recognitioncited by 24
- 2021Incorporating Domain Knowledge for Extractive Summarization of Legal Case Documentscited by 23
- 2021Play the Shannon Game With Language Models: A Human-Free Approach to Summary Evaluationcited by 12
- 2018Multi-Task Deep Learning for Legal Document Translation, Summarization and Multi-Label Classificationcited by 6
- 2023Question-Answering Approach to Evaluating Legal Summariescited by 5
- 2023On the State of German (Abstractive) Text Summarizationcited by 1
Built on by
Not cited yet.