fastNLP/reproduction/Summarization/README.md

# Summarization 

## Extractive Summarization


### Models

FastNLP中实现的模型包括：

1. Get To The Point: Summarization with Pointer-Generator Networks (See et al. 2017)
2. Searching for Effective Neural Extractive Summarization  What Works and What's Next (Zhong et al. 2019)
3. Fine-tune BERT for Extractive Summarization (Liu et al. 2019)




### Dataset

这里提供的摘要任务数据集包括：

- CNN/DailyMail
- Newsroom
- The New York Times Annotated Corpus
    - NYT
    - NYT50
- DUC
    - 2002 Task4
    - 2003/2004 Task1
- arXiv
- PubMed


其中公开数据集(CNN/DailyMail, Newsroom, arXiv, PubMed)预处理之后的下载地址：

- [百度云盘](https://pan.baidu.com/s/11qWnDjK9lb33mFZ9vuYlzA) (提取码：h1px)
- [Google Drive](https://drive.google.com/file/d/1uzeSdcLk5ilHaUTeJRNrf-_j59CQGe6r/view?usp=drivesdk)

未公开数据集(NYT, NYT50, DUC)数据处理部分脚本放置于data文件夹



### Dataset_loader

- SummarizationLoader: 用于读取处理好的jsonl格式数据集，返回以下field
    - text: 文章正文
    - summary: 摘要
    - domain: 可选，文章发布网站
    - tag: 可选，文章内容标签
    - labels: 抽取式句子标签

- BertSumLoader：用于读取作为 BertSum（Liu 2019） 输入的数据集，返回以下 field：
  - article：每篇文章被截断为 512 后的词表 ID
  - segmet_id：每句话属于 0/1 的 segment
  - cls_id：输入中 ‘[CLS]’ 的位置
  - label：抽取式句子标签






### Performance and Hyperparameters

|              Model              | ROUGE-1 | ROUGE-2 | ROUGE-L |                    Paper                    |
| :-----------------------------: | :-----: | :-----: | :-----: | :-----------------------------------------: |
|             LEAD 3              |  40.11  |  17.64  |  36.32  |            our data pre-process             |
|             ORACLE              |  55.24  |  31.14  |  50.96  |            our data pre-process             |
|    LSTM + Sequence Labeling     |  40.72  |  18.27  |  36.98  |                                             |
| Transformer + Sequence Labeling |  40.86  |  18.38  |  37.18  |                                             |
|     LSTM + Pointer Network      |    -    |    -    |    -    |                                             |
|  Transformer + Pointer Network  |    -    |    -    |    -    |                                             |
|             BERTSUM             |  42.71  |  19.76  |  39.03  | Fine-tune BERT for Extractive Summarization |
|         LSTM+PN+BERT+RL         |    -    |    -    |    -    |                                             |



## Abstractive Summarization
Still in Progress...