Datasets:
| language: | |
| - en | |
| multilinguality: | |
| - monolingual | |
| size_categories: | |
| - 10K<n<100K | |
| task_categories: | |
| - summarization | |
| - text-generation | |
| task_ids: [] | |
| tags: | |
| - conditional-text-generation | |
| dataset_info: | |
| config_name: document | |
| features: | |
| - name: report | |
| dtype: string | |
| - name: summary | |
| dtype: string | |
| splits: | |
| - name: train | |
| num_bytes: 953321013 | |
| num_examples: 17517 | |
| - name: validation | |
| num_bytes: 55820431 | |
| num_examples: 973 | |
| - name: test | |
| num_bytes: 51591123 | |
| num_examples: 973 | |
| download_size: 506610432 | |
| dataset_size: 1060732567 | |
| configs: | |
| - config_name: document | |
| data_files: | |
| - split: train | |
| path: document/train-* | |
| - split: validation | |
| path: document/validation-* | |
| - split: test | |
| path: document/test-* | |
| default: true | |
| # GovReport dataset for summarization | |
| Dataset for summarization of long documents.\ | |
| Adapted from this [repo](https://github.com/luyang-huang96/LongDocSum) and this [paper](https://arxiv.org/pdf/2104.02112.pdf)\ | |
| This dataset is compatible with the [`run_summarization.py`](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization) script from Transformers if you add this line to the `summarization_name_mapping` variable: | |
| ```python | |
| "ccdv/govreport-summarization": ("report", "summary") | |
| ``` | |
| ### Data Fields | |
| - `id`: paper id | |
| - `report`: a string containing the body of the report | |
| - `summary`: a string containing the summary of the report | |
| ### Data Splits | |
| This dataset has 3 splits: _train_, _validation_, and _test_. \ | |
| Token counts with a RoBERTa tokenizer. | |
| | Dataset Split | Number of Instances | Avg. tokens | | |
| | ------------- | --------------------|:----------------------| | |
| | Train | 17,517 | < 9,000 / < 500 | | |
| | Validation | 973 | < 9,000 / < 500 | | |
| | Test | 973 | < 9,000 / < 500 | | |
| # Cite original article | |
| ``` | |
| @misc{huang2021efficient, | |
| title={Efficient Attentions for Long Document Summarization}, | |
| author={Luyang Huang and Shuyang Cao and Nikolaus Parulian and Heng Ji and Lu Wang}, | |
| year={2021}, | |
| eprint={2104.02112}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CL} | |
| } | |
| ``` | |