multinews_dense_max

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dataset, except the input source documents of its `test` split have been replaced by a __dense__ retriever.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==10`

Retrieval results on the `train` set:

Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8661 | 0.6867 | 0.2118 | 0.7966 |

Retrieval results on the `validation` set:

Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8626 | 0.6859 | 0.2083 | 0.7949 |

Retrieval results on the `test` set:

Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.8625 | 0.6927 | 0.2096 | 0.7971 |

本数据集为[Multi-News](https://huggingface.co/datasets/multi_news)的复刻版本，仅将其`test`数据集划分（split）的输入源文档替换为**稠密检索器（dense retriever）**所获取的内容。所采用的检索流程如下：
- **查询（query）**：每个示例的`summary`字段
- **语料库（corpus）**：`train`、`validation`与`test`数据集划分下的全部文档的并集
- **检索器（retriever）**：基于PyTerrier默认配置实现的[`facebook/contriever-msmarco`](https://huggingface.co/facebook/contriever-msmarco)
- **Top-k策略（top-k strategy）**：`"max"`，即检索文档的数量`k`被设置为该数据集所有示例中所见的最大文档数，本次场景下`k=10`

训练集检索结果：
| 召回率@100（Recall@100） | R-precision（Rprec） | 精确率@k（Precision@k） | 召回率@k（Recall@k） |
| ----------- | ----------- | ----------- | ----------- |
| 0.8661 | 0.6867 | 0.2118 | 0.7966 |

验证集检索结果：
| 召回率@100（Recall@100） | R-precision（Rprec） | 精确率@k（Precision@k） | 召回率@k（Recall@k） |
| ----------- | ----------- | ----------- | ----------- |
| 0.8626 | 0.6859 | 0.2083 | 0.7949 |

测试集检索结果：
| 召回率@100（Recall@100） | R-precision（Rprec） | 精确率@k（Precision@k） | 召回率@k（Recall@k） |
| ----------- | ----------- | ----------- | ----------- |
| 0.8625 | 0.6927 | 0.2096 | 0.7971 |