# Summarisation of German Judgments in conjunction with a Class-based Evaluation

arXiv: https://arxiv.org/abs/2505.05947 · HTML: https://mlsanity.com/paper/2505.05947/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 38 references that are in the map.
- First posted on arXiv 9 May 2025.

## Builds on (most cited first, 11 of 11)

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation](https://mlsanity.com/paper/2305.14251/) (2023)
- [BARTScore: Evaluating Generated Text as Text Generation](https://mlsanity.com/paper/2106.11520/) (2021)
- [SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents](https://mlsanity.com/paper/1611.04230/) (2016)
- [A Dataset of German Legal Documents for Named Entity Recognition](https://mlsanity.com/paper/2003.13016/) (2020)
- [Incorporating Domain Knowledge for Extractive Summarization of Legal Case Documents](https://mlsanity.com/paper/2106.15876/) (2021)
- [Play the Shannon Game With Language Models: A Human-Free Approach to Summary Evaluation](https://mlsanity.com/paper/2103.10918/) (2021)
- [Multi-Task Deep Learning for Legal Document Translation, Summarization and Multi-Label Classification](https://mlsanity.com/paper/1810.07513/) (2018)
- [Question-Answering Approach to Evaluating Legal Summaries](https://mlsanity.com/paper/2309.15016/) (2023)
- [On the State of German (Abstractive) Text Summarization](https://mlsanity.com/paper/2301.07095/) (2023)

## 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.