Datasets:

allenai
/

ms2_dense_mean

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
task_ids: []
paperswithcode_id: multi-document-summarization
pretty_name: MSLR Shared Task
tags:
  - query-based-summarization
  - query-based-multi-document-summarization
  - scientific-document-summarization

This is a copy of the MS^2 dataset, except the input source documents of its validation split have been replaced by a dense retriever. The retrieval pipeline used:

query: The background 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: facebook/contriever-msmarco 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 validation set:

ndcg	recall@100	recall@1000	Rprec
0.4565	0.4364	0.728	0.2133