
This is PeerSum, a multi-document summarization dataset in the peer-review domain. More details can be found in the paper accepted at EMNLP 2023, [Summarizing Multiple Documents with Conversational Structure for Meta-review Generation](https://arxiv.org/abs/2305.01498). The original code and datasets are public on [GitHub](https://github.com/oaimli/PeerSum).

Please use the following code to download the dataset with the datasets library from Huggingface.
```python
from datasets import load_dataset
peersum_all = load_dataset('oaimli/PeerSum', split='all')
peersum_train = peersum_all.filter(lambda s: s['label'] == 'train')
peersum_val = peersum_all.filter(lambda s: s['label'] == 'val')
peersum_test = peersum_all.filter(lambda s: s['label'] == 'test')
```

The Huggingface dataset is mainly for multi-document summarization. Each sample comprises information with the following keys:
```
* paper_id: str (a link to the raw data)
* paper_title: str
* paper_abstract, str
* paper_acceptance, str
* meta_review, str
* review_ids, list(str)
* review_writers, list(str)
* review_contents, list(str)
* review_ratings, list(int)
* review_confidences, list(int)
* review_reply_tos, list(str)
* label, str, (train, val, test)
```

You can also download the raw data from [Google Drive](https://drive.google.com/drive/folders/1SGYvxY1vOZF2MpDn3B-apdWHCIfpN2uB?usp=sharing). The raw data comprises more information and it can be used for other analysis for peer reviews.
task_categories:summarization size_categories:10K<n<100K language:en license:apache-2.0 arxiv:2305.01498 region:us
{"language": ["en"], "license": "apache-2.0", "size_categories": ["10K<n<100K"], "task_categories": ["summarization"], "pretty_name": "PeerSum"}
TAGS
#task_categories-summarization #size_categories-10K<n<100K #language-English #license-apache-2.0 #arxiv-2305.01498 #region-us 


This is PeerSum, a multi-document summarization dataset in the peer-review domain. More details can be found in the paper accepted at EMNLP 2023, Summarizing Multiple Documents with Conversational Structure for Meta-review Generation. The original code and datasets are public on GitHub.

Please use the following code to download the dataset with the datasets library from Huggingface.


The Huggingface dataset is mainly for multi-document summarization. Each sample comprises information with the following keys:


You can also download the raw data from Google Drive. The raw data comprises more information and it can be used for other analysis for peer reviews.
TAGS
#task_categories-summarization #size_categories-10K<n<100K #language-English #license-apache-2.0 #arxiv-2305.01498 #region-us 
