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.gitattributes

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README.md

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----
-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dataset, except the input source documents of its `test` split have been replaced by a __sparse__ retriever.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__: BM25 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 |
-| ----------- | ----------- | ----------- | ----------- |
-| 0.5482 | 0.2243 | 0.0547 | 0.4063 |
-
-Retrieval results on the `validation` set:
-
-| Recall@100 | Rprec | Precision@k | Recall@k |
-| ----------- | ----------- | ----------- | ----------- |
-| 0.5476 | 0.2209 | 0.0553 | 0.4026 |
-
-Retrieval results on the `test` set:
-
-| Recall@100 | Rprec | Precision@k | Recall@k |
-| ----------- | ----------- | ----------- | ----------- |
-| 0.5480 | 0.2272 | 0.055 | 0.4039 |

data/validation-00000-of-00001-666f8f42dff99a14.parquet → allenai--multixscience_sparse_max/parquet-test.parquet

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data/test-00000-of-00001-f586e99c0013eedb.parquet → allenai--multixscience_sparse_max/parquet-train.parquet

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data/train-00000-of-00001-e71278f311282096.parquet → allenai--multixscience_sparse_max/parquet-validation.parquet

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dataset_infos.json

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-{"allenai--multixscience_sparse_max": {"description": "\nMulti-XScience, a large-scale multi-document summarization dataset created from scientific articles. Multi-XScience introduces a challenging multi-document summarization task: writing the related-work section of a paper based on its abstract and the articles it references.\n", "citation": "\n@article{lu2020multi,\n  title={Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles},\n  author={Lu, Yao and Dong, Yue and Charlin, Laurent},\n  journal={arXiv preprint arXiv:2010.14235},\n  year={2020}\n}\n", "homepage": "https://github.com/yaolu/Multi-XScience", "license": "", "features": {"aid": {"dtype": "string", "id": null, "_type": "Value"}, "mid": {"dtype": "string", "id": null, "_type": "Value"}, "abstract": {"dtype": "string", "id": null, "_type": "Value"}, "related_work": {"dtype": "string", "id": null, "_type": "Value"}, "ref_abstract": {"feature": {"cite_N": {"dtype": "string", "id": null, "_type": "Value"}, "mid": {"dtype": "string", "id": null, "_type": "Value"}, "abstract": {"dtype": "string", "id": null, "_type": "Value"}}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "multi_x_science_sum", "config_name": "default", "version": {"version_str": "1.1.0", "description": null, "major": 1, "minor": 1, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 169364305, "num_examples": 30369, "dataset_name": "multixscience_sparse_max"}, "test": {"name": "test", "num_bytes": 129111230, "num_examples": 5093, "dataset_name": "multixscience_sparse_max"}, "validation": {"name": "validation", "num_bytes": 28168458, "num_examples": 5066, "dataset_name": "multixscience_sparse_max"}}, "download_checksums": null, "download_size": 147636959, "post_processing_size": null, "dataset_size": 326643993, "size_in_bytes": 474280952}}