multinews_sparse_mean

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dataset, except the input source documents of its `test` split have been replaced by a __sparse__ retriever.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 |

本数据集为[Multi-News](https://huggingface.co/datasets/multi_news)数据集的复刻版本，仅其`test`划分的输入源文档已通过**稀疏检索器（sparse retriever）**完成替换。所采用的检索流程如下：
- **查询（query）**：每个样本的`summary`字段
- **语料库（corpus）**：`train`、`validation`与`test`划分的全部文档的并集
- **检索器（retriever）**：基于[PyTerrier](https://pyterrier.readthedocs.io/en/latest/)实现的BM25检索模型，使用默认配置参数
- **Top-k策略（top-k strategy）**：采用"mean"（均值）策略，即检索文档数`k`设为该数据集各样本中实际出现的文档数的平均值，本次场景下`k=3`

训练集（`train` set）检索结果如下：
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8793 | 0.7460 | 0.6403 | 0.7417 |

验证集（`validation` set）检索结果如下：
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8748 | 0.7453 | 0.6361 | 0.7442 |

测试集（`test` set）检索结果如下：
| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8775 | 0.7480 | 0.6370 | 0.7443 |