CourtPressGER: A German Court Decision to Press Release Summarization Dataset
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915665877663744 |
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| author | Nagl, Sebastian Elganayni, Mohamed Pospisil, Melanie Grabmair, Matthias |
| author_facet | Nagl, Sebastian Elganayni, Mohamed Pospisil, Melanie Grabmair, Matthias |
| contents | Official court press releases from Germany's highest courts present and explain judicial rulings to the public, as well as to expert audiences. Prior NLP efforts emphasize technical headnotes, ignoring citizen-oriented communication needs. We introduce CourtPressGER, a 6.4k dataset of triples: rulings, human-drafted press releases, and synthetic prompts for LLMs to generate comparable releases. This benchmark trains and evaluates LLMs in generating accurate, readable summaries from long judicial texts. We benchmark small and large LLMs using reference-based metrics, factual-consistency checks, LLM-as-judge, and expert ranking. Large LLMs produce high-quality drafts with minimal hierarchical performance loss; smaller models require hierarchical setups for long judgments. Initial benchmarks show varying model performance, with human-drafted releases ranking highest. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_09434 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CourtPressGER: A German Court Decision to Press Release Summarization Dataset Nagl, Sebastian Elganayni, Mohamed Pospisil, Melanie Grabmair, Matthias Computation and Language Artificial Intelligence Official court press releases from Germany's highest courts present and explain judicial rulings to the public, as well as to expert audiences. Prior NLP efforts emphasize technical headnotes, ignoring citizen-oriented communication needs. We introduce CourtPressGER, a 6.4k dataset of triples: rulings, human-drafted press releases, and synthetic prompts for LLMs to generate comparable releases. This benchmark trains and evaluates LLMs in generating accurate, readable summaries from long judicial texts. We benchmark small and large LLMs using reference-based metrics, factual-consistency checks, LLM-as-judge, and expert ranking. Large LLMs produce high-quality drafts with minimal hierarchical performance loss; smaller models require hierarchical setups for long judgments. Initial benchmarks show varying model performance, with human-drafted releases ranking highest. |
| title | CourtPressGER: A German Court Decision to Press Release Summarization Dataset |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2512.09434 |