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+ ---
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+ annotations_creators:
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+ - expert-generated
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+ language_creators:
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+ - expert-generated
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+ language:
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+ - en
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+ license:
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+ - other
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+ multilinguality:
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+ - monolingual
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+ pretty_name: Multi-News
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+ size_categories:
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+ - 10K<n<100K
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+ source_datasets:
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+ - original
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+ task_categories:
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+ - summarization
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+ task_ids:
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+ - news-articles-summarization
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+ paperswithcode_id: multi-news
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+ train-eval-index:
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+ - config: default
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+ task: summarization
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+ task_id: summarization
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+ splits:
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+ train_split: train
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+ eval_split: test
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+ col_mapping:
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+ document: text
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+ summary: target
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+ metrics:
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+ - type: rouge
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+ name: Rouge
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+ ---
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+
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+ 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 __dense__ retriever. The retrieval pipeline used:
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+
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+ - __query__: The `summary` field of each example
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+ - __corpus__: The union of all documents in the `train`, `validation` and `test` splits
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+ - __retriever__: [`facebook/contriever-msmarco`](https://huggingface.co/facebook/contriever-msmarco) via [PyTerrier](https://pyterrier.readthedocs.io/en/latest/) with default settings
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+ - __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
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+
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+ Retrieval results on the `test` set:
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+
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+ |ndcg | recall@100 | recall@1000 | Rprec |
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+ | ----------- | ----------- | ----------- | ----------- |
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+ | 0.815 | 0.8625 | 0.8907 | 0.6927 |