multixscience_sparse_mean

This is a copy of the [Multi-XScience](https://huggingface.co/datasets/multi_x_science_sum) dataset, except the input source documents of its `test` split have been replaced by a __sparse__ retriever. The retrieval pipeline used:

- __query__: The `related_work` 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==4`

Retrieval results on the `train` set:

| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.5482 | 0.2243 | 0.1578 | 0.2689 |

Retrieval results on the `validation` set:

| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.5476 | 0.2209 | 0.1592 | 0.2650 |

Retrieval results on the `test` set:

| Recall@100 | Rprec | Precision@k | Recall@k |
| ----------- | ----------- | ----------- | ----------- |
| 0.548 | 0.2272 | 0.1611 | 0.2704 |

本数据集为[Multi-XScience](https://huggingface.co/datasets/multi_x_science_sum)数据集的副本，仅将其`test`划分的输入源文档替换为**稀疏检索器（sparse retriever）**所获取的内容。所采用的检索流程如下：
- **查询（query）**：每个样本的`related_work`字段
- **语料库（corpus）**：`train`、`validation`与`test`划分下的全部文档的并集
- **检索器（retriever）**：基于[PyTerrier](https://pyterrier.readthedocs.io/en/latest/)实现的BM25检索模型，采用默认参数配置
- **top-k策略**：采用"mean"策略，即检索返回的文档数`k`设置为该数据集所有样本中平均出现的文档数量，本次任务中`k=4`

训练集检索结果：
| 召回率@100（Recall@100） | R准确率（Rprec） | 精确率@k（Precision@k） | 召回率@k（Recall@k） |
| ----------- | ----------- | ----------- | ----------- |
| 0.5482 | 0.2243 | 0.1578 | 0.2689 |

验证集检索结果：
| 召回率@100（Recall@100） | R准确率（Rprec） | 精确率@k（Precision@k） | 召回率@k（Recall@k） |
| ----------- | ----------- | ----------- | ----------- |
| 0.5476 | 0.2209 | 0.1592 | 0.2650 |

测试集检索结果：
| 召回率@100（Recall@100） | R准确率（Rprec） | 精确率@k（Precision@k） | 召回率@k（Recall@k） |
| ----------- | ----------- | ----------- | ----------- |
| 0.548 | 0.2272 | 0.1611 | 0.2704 |