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metadata
annotations_creators:
  - found
language_creators:
  - found
language:
  - en
license:
  - unknown
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100K
source_datasets:
  - original
task_categories:
  - summarization
paperswithcode_id: multi-xscience
pretty_name: Multi-XScience

This is a copy of the Multi-XScience dataset, except the input source documents of its test split have been replaced by a dense retriever. The retrieval pipeline used:

  • query: The related_work 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: "max", i.e. the number of documents retrieved, k, is set as the maximum number of documents seen across examples in this dataset, in this case k==20

Retrieval results on the train set:

Recall@100 Rprec Precision@k Recall@k
0.5270 0.2005 0.0573 0.3785

Retrieval results on the validation set:

Recall@100 Rprec Precision@k Recall@k
0.5310 0.2026 0.059 0.3831

Retrieval results on the test set:

Recall@100 Rprec Precision@k Recall@k
0.5229 0.2081 0.058 0.3794