metadata

language:
  - en
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
task_categories:
  - summarization
  - text2text-generation
task_ids: []
tags:
  - conditional-text-generation

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}
}