README.md

metadata

annotations_creators:
  - expert-generated
language_creators:
  - expert-generated
language:
  - en
license:
  - other
multilinguality:
  - monolingual
pretty_name: WCEP-10
size_categories:
  - 1K<n<10K
source_datasets:
  - original
task_categories:
  - summarization
task_ids:
  - news-articles-summarization
paperswithcode_id: wcep
train-eval-index:
  - config: default
    task: summarization
    task_id: summarization
    splits:
      train_split: train
      eval_split: test
    col_mapping:
      document: text
      summary: target
    metrics:
      - type: rouge
        name: Rouge

This is a copy of the WCEP-10 dataset, except the input source documents of the train, validation, and test splits have been replaced by a dense retriever. The retrieval pipeline used:

• query: The summary field of each example
• corpus: The union of all documents in the train, validation and test splits
• retriever: facebook/contriever-msmarco via PyTerrier with default settings
• top-k strategy: "oracle", i.e. the number of documents retrieved, k, is set as the original number of input documents for each example Retrieval results on the train set:

Recall@100	Rprec	Precision@k	Recall@k
0.8590	0.6490	0.6490	0.6490

Retrieval results on the validation set:

Recall@100	Rprec	Precision@k	Recall@k
0.8578	0.6326	0.6326	0.6326

Retrieval results on the test set:

Recall@100	Rprec	Precision@k	Recall@k
0.8678	0.6631	0.6631	0.6631