Datasets:
Tasks:
Summarization
Sub-tasks:
news-articles-summarization
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
expert-generated
Annotations Creators:
expert-generated
Source Datasets:
original
Tags:
License:
| 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dataset, except the input source documents of its `test` split have been replaced by a __sparse__ retriever.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 | |