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README.md

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----
-annotations_creators:
-- expert-generated
-language_creators:
-- expert-generated
-language:
-- en
-license:
-- other
-multilinguality:
-- monolingual
-pretty_name: Multi-News
-size_categories:
-- 10K<n<100K
-source_datasets:
-- original
-task_categories:
-- summarization
-task_ids:
-- news-articles-summarization
-paperswithcode_id: multi-news
-train-eval-index:
-- config: default
-  task: summarization
-  task_id: summarization
-  splits:
-    train_split: train
-    eval_split: test
-  col_mapping:
-    document: text
-    summary: target
-  metrics:
-    - type: rouge
-      name: Rouge
----
-
-This is a copy of the [Multi-News](https://huggingface.co/datasets/multi_news) dataset, except the input source documents of its `test` split have been replaced by a __sparse__ retriever. The retrieval pipeline used:
-
-- __query__: The `summary` 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__: `"mean"`, i.e. the number of documents retrieved, `k`, is set as the mean number of documents seen across examples in this dataset, in this case `k==3`
-
-Retrieval results on the `train` set:
-
-| Recall@100 | Rprec | Precision@k | Recall@k |
-| ----------- | ----------- | ----------- | ----------- |
-| 0.8793 | 0.7460 | 0.6403 | 0.7417 |
-
-Retrieval results on the `validation` set:
-
-| Recall@100 | Rprec | Precision@k | Recall@k |
-| ----------- | ----------- | ----------- | ----------- |
-| 0.8748 | 0.7453 | 0.6361 | 0.7442 |
-
-Retrieval results on the `test` set:
-
-| Recall@100 | Rprec | Precision@k | Recall@k |
-| ----------- | ----------- | ----------- | ----------- |
-| 0.8775 | 0.7480 | 0.6370 | 0.7443 |

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dataset_infos.json

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-{"allenai--multinews_sparse_mean": {"description": "\nMulti-News, consists of news articles and human-written summaries\nof these articles from the site newser.com.\nEach summary is professionally written by editors and\nincludes links to the original articles cited.\n\nThere are two features:\n  - document: text of news articles seperated by special token \"|||||\".\n  - summary: news summary.\n", "citation": "\n@misc{alex2019multinews,\n    title={Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model},\n    author={Alexander R. Fabbri and Irene Li and Tianwei She and Suyi Li and Dragomir R. Radev},\n    year={2019},\n    eprint={1906.01749},\n    archivePrefix={arXiv},\n    primaryClass={cs.CL}\n}\n", "homepage": "https://github.com/Alex-Fabbri/Multi-News", "license": "For non-commercial research and educational purposes only", "features": {"document": {"dtype": "string", "id": null, "_type": "Value"}, "summary": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "document", "output": "summary"}, "task_templates": null, "builder_name": "multi_news", "config_name": "default", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 558392265, "num_examples": 44972, "dataset_name": "multinews_sparse_mean"}, "validation": {"name": "validation", "num_bytes": 68272432, "num_examples": 5622, "dataset_name": "multinews_sparse_mean"}, "test": {"name": "test", "num_bytes": 100219926, "num_examples": 5622, "dataset_name": "multinews_sparse_mean"}}, "download_checksums": null, "download_size": 0, "post_processing_size": null, "dataset_size": 726884623, "size_in_bytes": 726884623}}