README.md

metadata

annotations_creators:
  - expert-generated
language_creators:
  - expert-generated
language:
  - en
license:
  - apache-2.0
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100K
source_datasets:
  - extended|other-MS^2
  - extended|other-Cochrane
task_categories:
  - summarization
  - text2text-generation
paperswithcode_id: multi-document-summarization
pretty_name: MSLR Shared Task

This is a copy of the Cochrane dataset, except the input source documents of its validation split have been replaced by a sparse retriever. The retrieval pipeline used:

• query: The target field of each example
• corpus: The union of all documents in the train, validation and test splits. A document is the concatenation of the title and abstract.
• 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==9

Retrieval results on the train set:

Recall@100	Rprec	Precision@k	Recall@k
0.7014	0.3841	0.2976	0.4157

Retrieval results on the validation set:

Recall@100	Rprec	Precision@k	Recall@k
0.7226	0.4023	0.3095	0.4443

Retrieval results on the test set:

N/A. Test set is blind so we do not have any queries.