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Datasets:
ccdv
/
WCEP-10

Tasks:
Summarization
Languages:
English
Size:
1K<n<10K
ArXiv:
Tags:
conditional-text-generation
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YAML Metadata Warning: The task_categories "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

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:

"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}
}
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