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-{"allenai--multi_x_science_sum_sparse": {"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": "multi_x_science_sum_sparse"}, "test": {"name": "test", "num_bytes": 33940993, "num_examples": 5093, "dataset_name": "multi_x_science_sum_sparse"}, "validation": {"name": "validation", "num_bytes": 28168458, "num_examples": 5066, "dataset_name": "multi_x_science_sum_sparse"}}, "download_checksums": null, "download_size": 101198298, "post_processing_size": null, "dataset_size": 231473756, "size_in_bytes": 332672054}}
+{"allenai--multixscience_sparse_oracle": {"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_oracle"}, "test": {"name": "test", "num_bytes": 34629565, "num_examples": 5093, "dataset_name": "multixscience_sparse_oracle"}, "validation": {"name": "validation", "num_bytes": 28168458, "num_examples": 5066, "dataset_name": "multixscience_sparse_oracle"}}, "download_checksums": null, "download_size": 12896663, "post_processing_size": null, "dataset_size": 232162328, "size_in_bytes": 245058991}}