add readme

README.md

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+---
+languages:
+- en
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+task_categories:
+- conditional-text-generation
+task_ids:
+- summarization
+---
+
+# GovReport dataset for summarization
+
+Dataset for summarization of long documents.\
+Adapted from this [repo](https://github.com/luyang-huang96/LongDocSum).\
+Note that original data are pre-tokenized so this dataset returns " ".join(text).\
+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}
+    }
+```
+