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--- |
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dataset_info: |
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features: |
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- name: text |
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dtype: string |
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- name: summary |
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dtype: string |
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splits: |
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- name: train |
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|
num_bytes: 1483542519.7144387 |
|
|
num_examples: 55993 |
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|
- name: validation |
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|
num_bytes: 185439503.0714796 |
|
|
num_examples: 6999 |
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|
- name: test |
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|
num_bytes: 185465998.21408162 |
|
|
num_examples: 7000 |
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|
download_size: 904301529 |
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dataset_size: 1854448020.9999998 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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- split: validation |
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path: data/validation-* |
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- split: test |
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path: data/test-* |
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task_categories: |
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- summarization |
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language: |
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- en |
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size_categories: |
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- 10K<n<100K |
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--- |
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# LegalSumm: A Comprehensive Legal Document Summarization Dataset |
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LegalSumm is a merged dataset combining three prominent legal document collections (GovReport, BillSum, and CaseSumm) to create a comprehensive resource for training and evaluating legal text summarisation models. |
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## Dataset Composition |
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**Total samples:** 69,992 document-summary pairs |
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**Sources:** |
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- GovReport: 19,466 U.S. government reports (27.8%) |
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- BillSum: 23,455 U.S. Congressional bills (33.5%) |
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- CaseSumm: 27,071 U.S. Supreme Court opinions (38.7%) |
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## Features |
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- **text**: Original legal document (full text) |
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- **summary**: Human-written summary of the document |
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## Preprocessing |
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Documents have been standardized to ensure consistent formatting across different legal document types. |
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All samples have been randomly shuffled and split into training (80%), validation (10%), and test (10%) sets. |
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