Datasets:
Tasks:
Summarization
Modalities:
Text
Formats:
parquet
Sub-tasks:
news-articles-summarization
Languages:
English
Size:
10K - 100K
License:
Commit
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77d6484
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Parent(s):
fccb66b
Update parquet files
Browse files- .gitattributes +0 -51
- README.md +0 -60
- data/validation-00000-of-00001-6d9e90279dc820aa.parquet → allenai--wcep_dense_max/parquet-test.parquet +2 -2
- data/test-00000-of-00001-b9b9f817a4ea62c7.parquet → allenai--wcep_dense_max/parquet-train.parquet +2 -2
- data/train-00000-of-00001-af48311c4f368aff.parquet → allenai--wcep_dense_max/parquet-validation.parquet +2 -2
- dataset_infos.json +0 -1
.gitattributes
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README.md
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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: WCEP-10
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size_categories:
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- 1K<n<10K
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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: wcep
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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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This is a copy of the [WCEP-10](https://huggingface.co/datasets/ccdv/WCEP-10) 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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- __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, in this case `k==10`
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Retrieval results on the `train` set:
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| Recall@100 | Rprec | Precision@k | Recall@k |
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| ----------- | ----------- | ----------- | ----------- |
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| 0.8590 | 0.6490 | 0.5967 | 0.6631 |
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Retrieval results on the `validation` set:
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| Recall@100 | Rprec | Precision@k | Recall@k |
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| ----------- | ----------- | ----------- | ----------- |
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| 0.8578 | 0.6326 | 0.6040 | 0.6401 |
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Retrieval results on the `test` set:
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| Recall@100 | Rprec | Precision@k | Recall@k |
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| ----------- | ----------- | ----------- | ----------- |
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| 0.8678 | 0.6631 | 0.6301 | 0.6740 |
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data/validation-00000-of-00001-6d9e90279dc820aa.parquet → allenai--wcep_dense_max/parquet-test.parquet
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data/test-00000-of-00001-b9b9f817a4ea62c7.parquet → allenai--wcep_dense_max/parquet-train.parquet
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data/train-00000-of-00001-af48311c4f368aff.parquet → allenai--wcep_dense_max/parquet-validation.parquet
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dataset_infos.json
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{"allenai--wcep_dense_max": {"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": 192494942, "num_examples": 8158, "dataset_name": "wcep_dense_max"}, "validation": {"name": "validation", "num_bytes": 25092691, "num_examples": 1020, "dataset_name": "wcep_dense_max"}, "test": {"name": "test", "num_bytes": 24679376, "num_examples": 1022, "dataset_name": "wcep_dense_max"}}, "download_checksums": null, "download_size": 116982084, "post_processing_size": null, "dataset_size": 242267009, "size_in_bytes": 359249093}}
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