A Comprehensive Survey on Legal Summarization: Challenges and Future Directions
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866913670700728320 |
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| author | Akter, Mousumi Çano, Erion Weber, Erik Dobler, Dennis Habernal, Ivan |
| author_facet | Akter, Mousumi Çano, Erion Weber, Erik Dobler, Dennis Habernal, Ivan |
| contents | This article provides a systematic up-to-date survey of automatic summarization techniques, datasets, models, and evaluation methods in the legal domain. Through specific source selection criteria, we thoroughly review over 120 papers spanning the modern `transformer' era of natural language processing (NLP), thus filling a gap in existing systematic surveys on the matter. We present existing research along several axes and discuss trends, challenges, and opportunities for future research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_17830 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Comprehensive Survey on Legal Summarization: Challenges and Future Directions Akter, Mousumi Çano, Erion Weber, Erik Dobler, Dennis Habernal, Ivan Computation and Language This article provides a systematic up-to-date survey of automatic summarization techniques, datasets, models, and evaluation methods in the legal domain. Through specific source selection criteria, we thoroughly review over 120 papers spanning the modern `transformer' era of natural language processing (NLP), thus filling a gap in existing systematic surveys on the matter. We present existing research along several axes and discuss trends, challenges, and opportunities for future research. |
| title | A Comprehensive Survey on Legal Summarization: Challenges and Future Directions |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2501.17830 |