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split have been replaced by a sparse retriever.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
andtest
splits. A document is the concatenation of thetitle
andabstract
. - retriever: BM25 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 |
---|---|---|---|
0.7014 | 0.3841 | 0.3841 | 0.3841 |
Retrieval results on the validation
set:
Recall@100 | Rprec | Precision@k | Recall@k |
---|---|---|---|
0.7226 | 0.4023 | 0.4023 | 0.4023 |
Retrieval results on the test
set:
N/A. Test set is blind so we do not have any queries.