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---
language:
- en
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
task_categories:
- summarization
- text2text-generation
task_ids: []
tags:
- conditional-text-generation
---
# 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}
}
``` |