
# 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}
    }
```


task_categories:summarization task_categories:text-generation multilinguality:monolingual size_categories:10K<n<100K language:en conditional-text-generation arxiv:2104.02112 region:us
{"language": ["en"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "task_categories": ["summarization", "text-generation"], "task_ids": [], "tags": ["conditional-text-generation"]}
TAGS
#task_categories-summarization #task_categories-text-generation #multilinguality-monolingual #size_categories-10K<n<100K #language-English #conditional-text-generation #arxiv-2104.02112 #region-us 

GovReport dataset for summarization
===================================


Dataset for summarization of long documents.  

Adapted from this repo and this paper  

This dataset is compatible with the 'run\_summarization.py' script from Transformers if you add this line to the 'summarization\_name\_mapping' variable:


### 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.



Cite original article
=====================



### 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.



Cite original article
=====================
TAGS
#task_categories-summarization #task_categories-text-generation #multilinguality-monolingual #size_categories-10K<n<100K #language-English #conditional-text-generation #arxiv-2104.02112 #region-us 
 ### 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.



Cite original article
=====================
passage: TAGS
#task_categories-summarization #task_categories-text-generation #multilinguality-monolingual #size_categories-10K<n<100K #language-English #conditional-text-generation #arxiv-2104.02112 #region-us 
### 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.



Cite original article
=====================