
# MediaSum dataset for summarization

Summarization dataset copied from [MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization](https://github.com/zcgzcgzcg1/MediaSum)

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/mediasum": ("document", "summary")
```

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

Add `_prepended` to config name to prepend the speaker name before each dialogue: `speaker: text` \
Default is `roberta_prepended` (compatible with BART).

### 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         | 443596              |
| Validation    | 10000               |
| Test          | 10000               |


# Cite original article
```
@article{zhu2021mediasum,
  title={MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization},
  author={Zhu, Chenguang and Liu, Yang and Mei, Jie and Zeng, Michael},
  journal={arXiv preprint arXiv:2103.06410},
  year={2021}
}
```
task_categories:summarization task_categories:text2text-generation multilinguality:monolingual size_categories:100K<n<1M language:en conditional-text-generation region:us
{"language": ["en"], "multilinguality": ["monolingual"], "size_categories": ["100K<n<1M"], "task_categories": ["summarization", "text2text-generation"], "task_ids": [], "tags": ["conditional-text-generation"]}
TAGS
#task_categories-summarization #task_categories-text2text-generation #multilinguality-monolingual #size_categories-100K<n<1M #language-English #conditional-text-generation #region-us 

MediaSum dataset for summarization
==================================


Summarization dataset copied from MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization


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>"
* 'newline' will concatenate documents with "\n"
* 'bert' will concatenate documents with "[SEP]"
* 'list' will return the list of documents instead of a single string


Add '\_prepended' to config name to prepend the speaker name before each dialogue: 'speaker: text'   

Default is 'roberta\_prepended' (compatible with BART).


### 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-100K<n<1M #language-English #conditional-text-generation #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-100K<n<1M #language-English #conditional-text-generation #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
=====================