readme
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README.md
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Adapted from this [repo](https://github.com/armancohan/long-summarization).\
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Note that original data are pre-tokenized
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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:
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```python
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"ccdv/pubmed-summarization": ("article", "abstract")
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```
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# Cite original article
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```
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@inproceedings{cohan-etal-2018-discourse,
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Adapted from this [repo](https://github.com/armancohan/long-summarization).\
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Note that original data are pre-tokenized so this dataset returns " ".join(text).\
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This dataset returns .\
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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:
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```python
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"ccdv/pubmed-summarization": ("article", "abstract")
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```
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### Data Fields
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- `id`: paper id
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- `article`: a string containing the body of the paper
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- `abstract`: a string containing the abstract of the paper
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### Data Splits
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This dataset has 3 splits: _train_, _validation_, and _test_. \
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Token counts are white space based.
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| Dataset Split | Number of Instances | Avg. tokens |
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| ------------- | --------------------|:----------------------|
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| Train | 119,924 | 3043 / 215 |
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| Validation | 6,633 | 3111 / 216 |
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| Test | 6,658 | 3092 / 219 |
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# Cite original article
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```
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@inproceedings{cohan-etal-2018-discourse,
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