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--- |
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license: apache-2.0 |
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task_categories: |
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- summarization |
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language: |
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- en |
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pretty_name: PeerSum |
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size_categories: |
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- 10K<n<100K |
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--- |
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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). |
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Please use the following code to download the dataset with the datasets library from Huggingface. |
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```python |
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from datasets import load_dataset |
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peersum_all = load_dataset('oaimli/PeerSum', split='all') |
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peersum_train = peersum_all.filter(lambda s: s['label'] == 'train') |
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peersum_val = peersum_all.filter(lambda s: s['label'] == 'val') |
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peersum_test = peersum_all.filter(lambda s: s['label'] == 'test') |
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``` |
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The Huggingface dataset is mainly for multi-document summarization. Each sample comprises information with the following keys: |
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``` |
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* paper_id: str (a link to the raw data) |
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* paper_title: str |
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* paper_abstract, str |
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* paper_acceptance, str |
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* meta_review, str |
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* review_ids, list(str) |
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* review_writers, list(str) |
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* review_contents, list(str) |
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* review_ratings, list(int) |
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* review_confidences, list(int) |
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* review_reply_tos, list(str) |
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* label, str, (train, val, test) |
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``` |
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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. |
