
# WCEP10 dataset for summarization

Summarization dataset copied from [PRIMERA](https://github.com/allenai/PRIMER)

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/WCEP-10": ("document", "summary")
```

# Configs
4 possibles configs:
- `roberta` will concatenate documents with "\</s\>" (default)
- `newline` will concatenate documents with "\n"
- `bert` will concatenate documents with "[SEP]"
- `list` will return the list of documents instead of a string

### Data Fields

- `id`: paper id
- `document`: a string/list containing the body of a set of documents
- `summary`: a string containing the abstract of the set

### Data Splits

This dataset has 3 splits: _train_, _validation_, and _test_. \

| Dataset Split | Number of Instances | 
| ------------- | --------------------|
| Train         | 8158                |
| Validation    | 1020                |
| Test          | 1022                |


# Cite original article
```
@article{DBLP:journals/corr/abs-2005-10070,
    author    = {Demian Gholipour Ghalandari and
                Chris Hokamp and
                Nghia The Pham and
                John Glover and
                Georgiana Ifrim},
    title     = {A Large-Scale Multi-Document Summarization Dataset from the Wikipedia
                Current Events Portal},
    journal   = {CoRR},
    volume    = {abs/2005.10070},
    year      = {2020},
    url       = {https://arxiv.org/abs/2005.10070},
    eprinttype = {arXiv},
    eprint    = {2005.10070},
    timestamp = {Fri, 22 May 2020 16:21:28 +0200},
    biburl    = {https://dblp.org/rec/journals/corr/abs-2005-10070.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
    }

@article{DBLP:journals/corr/abs-2110-08499,
    author    = {Wen Xiao and
                Iz Beltagy and
                Giuseppe Carenini and
                Arman Cohan},
    title     = {{PRIMER:} Pyramid-based Masked Sentence Pre-training for Multi-document
                Summarization},
    journal   = {CoRR},
    volume    = {abs/2110.08499},
    year      = {2021},
    url       = {https://arxiv.org/abs/2110.08499},
    eprinttype = {arXiv},
    eprint    = {2110.08499},
    timestamp = {Fri, 22 Oct 2021 13:33:09 +0200},
    biburl    = {https://dblp.org/rec/journals/corr/abs-2110-08499.bib},
    bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
task_categories:summarization task_categories:text2text-generation multilinguality:monolingual size_categories:1K<n<10K language:en conditional-text-generation arxiv:2005.10070 arxiv:2110.08499 region:us
{"language": ["en"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "task_categories": ["summarization", "text2text-generation"], "task_ids": [], "tags": ["conditional-text-generation"]}
TAGS
#task_categories-summarization #task_categories-text2text-generation #multilinguality-monolingual #size_categories-1K<n<10K #language-English #conditional-text-generation #arxiv-2005.10070 #arxiv-2110.08499 #region-us 

WCEP10 dataset for summarization
================================


Summarization dataset copied from PRIMERA


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


Configs
=======


4 possibles configs:


* 'roberta' will concatenate documents with "</s>" (default)
* 'newline' will concatenate documents with "\n"
* 'bert' will concatenate documents with "[SEP]"
* 'list' will return the list of documents instead of a string


### Data Fields


* 'id': paper id
* 'document': a string/list containing the body of a set of documents
* 'summary': a string containing the abstract of the set


### Data Splits


This dataset has 3 splits: *train*, *validation*, and *test*. \



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



### Data Fields


* 'id': paper id
* 'document': a string/list containing the body of a set of documents
* 'summary': a string containing the abstract of the set ### Data Splits


This dataset has 3 splits: *train*, *validation*, and *test*. \



Cite original article
=====================
TAGS
#task_categories-summarization #task_categories-text2text-generation #multilinguality-monolingual #size_categories-1K<n<10K #language-English #conditional-text-generation #arxiv-2005.10070 #arxiv-2110.08499 #region-us 
 ### Data Fields


* 'id': paper id
* 'document': a string/list containing the body of a set of documents
* 'summary': a string containing the abstract of the set ### Data Splits


This dataset has 3 splits: *train*, *validation*, and *test*. \



Cite original article
=====================
passage: TAGS
#task_categories-summarization #task_categories-text2text-generation #multilinguality-monolingual #size_categories-1K<n<10K #language-English #conditional-text-generation #arxiv-2005.10070 #arxiv-2110.08499 #region-us 
### Data Fields


* 'id': paper id
* 'document': a string/list containing the body of a set of documents
* 'summary': a string containing the abstract of the set### Data Splits


This dataset has 3 splits: *train*, *validation*, and *test*. \



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