Datasets:
Tasks:
Summarization
Modalities:
Text
Formats:
parquet
Sub-tasks:
news-articles-summarization
Languages:
English
Size:
10K - 100K
License:
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dataset, except the input source documents of its `test` split have been replaced by a __sparse__ retriever.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==9` | |
Retrieval results on the `train` set: | |
| Recall@100 | Rprec | Precision@k | Recall@k | | |
| ----------- | ----------- | ----------- | ----------- | | |
| 0.8753 | 0.6443 | 0.6196 | 0.6237 | | |
Retrieval results on the `validation` set: | |
| Recall@100 | Rprec | Precision@k | Recall@k | | |
| ----------- | ----------- | ----------- | ----------- | | |
| 0.8706 | 0.6280 | 0.6260 | 0.5989 | | |
Retrieval results on the `test` set: | |
| Recall@100 | Rprec | Precision@k | Recall@k | | |
| ----------- | ----------- | ----------- | ----------- | | |
| 0.8836 | 0.6658 | 0.6601 | 0.6388 | |