Datasets:
Tasks:
Summarization
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K<n<100K
Tags:
License:
| 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](https://huggingface.co/datasets/multi_x_science_sum) dataset, except the input source documents of its `test` split have been replaced by a __dense__ retriever.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__: [`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, in this case `k==20` | |
| Retrieval results on the `train` set: | |
| | Recall@100 | Rprec | Precision@k | Recall@k | | |
| | ----------- | ----------- | ----------- | ----------- | | |
| | 0.5270 | 0.2005 | 0.0573 | 0.3785 | | |
| Retrieval results on the `validation` set: | |
| | Recall@100 | Rprec | Precision@k | Recall@k | | |
| | ----------- | ----------- | ----------- | ----------- | | |
| | 0.5310 | 0.2026 | 0.059 | 0.3831 | | |
| Retrieval results on the `test` set: | |
| | Recall@100 | Rprec | Precision@k | Recall@k | | |
| | ----------- | ----------- | ----------- | ----------- | | |
| | 0.5229 | 0.2081 | 0.058 | 0.3794 | |