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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: WCEP-10
-size_categories:
-- 1K<n<10K
-source_datasets:
-- original
-task_categories:
-- summarization
-task_ids:
-- news-articles-summarization
-paperswithcode_id: wcep
-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 [WCEP-10](https://huggingface.co/datasets/ccdv/WCEP-10) dataset, except the input source documents of its `train`, `validation, and `test` splits have been have been replaced by a __dense__ retriever.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__: [`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==9`
-
-Retrieval results on the `train` set:
-
-| Recall@100 | Rprec | Precision@k | Recall@k |
-| ----------- | ----------- | ----------- | ----------- |
-| 0.8590 | 0.6490 | 0.6239 | 0.6271 |
-
-Retrieval results on the `validation` set:
-
-| Recall@100 | Rprec | Precision@k | Recall@k |
-| ----------- | ----------- | ----------- | ----------- |
-| 0.8578 | 0.6326 | 0.6301 | 0.6031 |
-
-Retrieval results on the `test` set:
-
-| Recall@100 | Rprec | Precision@k | Recall@k |
-| ----------- | ----------- | ----------- | ----------- |
-| 0.8678 | 0.6631 | 0.6564 | 0.6338 |

data/validation-00000-of-00001-6a5d0ec0b63ea142.parquet → allenai--wcep_dense_mean/parquet-test.parquet

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data/test-00000-of-00001-4c65e1b4d1720a65.parquet → allenai--wcep_dense_mean/parquet-train.parquet

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dataset_infos.json

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-{"allenai--wcep_dense_mean": {"description": "\n WCEP10 dataset for summarization.\n From paper: \"A Large-Scale Multi-Document Summarization Dataset from the Wikipedia\n                Current Events Portal\" by D. Gholipour et al.\"\n From paper: \"PRIMER: Pyramid-based Masked Sentence Pre-training for Multi-document\n                Summarization\" by W. Xiao et al.\"\n\n", "citation": "    @article{DBLP:journals/corr/abs-2005-10070,\n    author    = {Demian Gholipour Ghalandari and\n                Chris Hokamp and\n                Nghia The Pham and\n                John Glover and\n                Georgiana Ifrim},\n    title     = {A Large-Scale Multi-Document Summarization Dataset from the Wikipedia\n                Current Events Portal},\n    journal   = {CoRR},\n    volume    = {abs/2005.10070},\n    year      = {2020},\n    url       = {https://arxiv.org/abs/2005.10070},\n    eprinttype = {arXiv},\n    eprint    = {2005.10070},\n    timestamp = {Fri, 22 May 2020 16:21:28 +0200},\n    biburl    = {https://dblp.org/rec/journals/corr/abs-2005-10070.bib},\n    bibsource = {dblp computer science bibliography, https://dblp.org}\n    }\n\n\n    @article{DBLP:journals/corr/abs-2110-08499,\n    author    = {Wen Xiao and\n                Iz Beltagy and\n                Giuseppe Carenini and\n                Arman Cohan},\n    title     = {{PRIMER:} Pyramid-based Masked Sentence Pre-training for Multi-document\n                Summarization},\n    journal   = {CoRR},\n    volume    = {abs/2110.08499},\n    year      = {2021},\n    url       = {https://arxiv.org/abs/2110.08499},\n    eprinttype = {arXiv},\n    eprint    = {2110.08499},\n    timestamp = {Fri, 22 Oct 2021 13:33:09 +0200},\n    biburl    = {https://dblp.org/rec/journals/corr/abs-2110-08499.bib},\n    bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n", "homepage": "https://github.com/allenai/PRIMER", "license": "", "features": {"document": {"dtype": "string", "id": null, "_type": "Value"}, "summary": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "wcep-10", "config_name": "roberta", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 176354211, "num_examples": 8158, "dataset_name": "wcep_dense_mean"}, "validation": {"name": "validation", "num_bytes": 21923579, "num_examples": 1020, "dataset_name": "wcep_dense_mean"}, "test": {"name": "test", "num_bytes": 21891779, "num_examples": 1022, "dataset_name": "wcep_dense_mean"}}, "download_checksums": null, "download_size": 107474041, "post_processing_size": null, "dataset_size": 220169569, "size_in_bytes": 327643610}}