| | --- |
| | 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](https://huggingface.co/datasets/allenai/mslr2022) dataset, except the input source documents of its `validation` split have been replaced by a __dense__ retriever.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`](https://huggingface.co/facebook/contriever-msmarco) via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) 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 |
| |
|
| | Retrieval results on the `validation` set: |
| |
|
| | |ndcg | recall@100 | recall@1000 | Rprec | |
| | | ----------- | ----------- | ----------- | ----------- | |
| | | 0.4565 | 0.4364 | 0.728 | 0.2133 | |
