README.md

metadata

annotations_creators:
  - expert-generated
language_creators:
  - expert-generated
language:
  - en
license:
  - other
multilinguality:
  - monolingual
pretty_name: Multi-News
size_categories:
  - 10K<n<100K
source_datasets:
  - original
task_categories:
  - summarization
task_ids:
  - news-articles-summarization
paperswithcode_id: multi-news
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This is a copy of the Multi-News dataset, except the input source documents of its test split have been replaced by a sparse retriever.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: BM25 via PyTerrier with default settings
• top-k strategy: "mean", i.e. the number of documents retrieved, k, is set as the mean number of documents seen across examples in this dataset

Retrieval results on the test set:

ndcg	recall@100	recall@1000	Rprec
0.8532	0.8775	0.8964	0.748