README.md

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This is a copy of the Multi-XScience dataset, except the input source documents of its test split have been replaced by a sparse retriever.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: 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, in this case k==4

Retrieval results on the train set:

Recall@100	Rprec	Precision@k	Recall@k
0.5482	0.2243	0.1578	0.2689

Retrieval results on the validation set:

Recall@100	Rprec	Precision@k	Recall@k
0.5476	0.2209	0.1592	0.2650

Retrieval results on the test set:

Recall@100	Rprec	Precision@k	Recall@k
0.548	0.2272	0.1611	0.2704