这个仓库是[字节跳动比赛 Byte Cup 2018,自动生成新闻标题](https://biendata.com/competition/bytecup2018/),水滴队(最终成绩23名)的代码。
有关比赛的总结,欢迎移步博客:https://blog.csdn.net/taoyafan/article/details/84879285
## Requirements
python3.5 或以上
tensorflow 1.12(接近的几个版本应该也可以)
## 程序说明
修改自程序[**RLSeq2Seq**](https://github.com/yaserkl/RLSeq2Seq)
主要改动如下:
(1)更换 python 版本为 python3。
(2)对 policy gradient 部分进行了大量的修改,原程序存在很多错误,如计算 ROUGE 时没有将 decode mask 去掉,前向计算时 greedy 和 sample 没有分开,decode 的输入也是一样的。
(3)训练的同时增加 eval,并保存在验证集效果最好的最后三个模型。
(4)增加对[**pointer-generator**](https://github.com/abisee/pointer-generator)的模型的兼容性,可以直接使用其预训练模型。
(5)对 policy gradient 的修改,将论文[A Deep Reinforced Model for Abstractive Summarization](https://arxiv.org/abs/1705.04304)中的公式(15)改为
$$
L_{rl}=(r(y^{s}) - r(y^{g}))\sum_{t=1}^{n'}{\rm log}p(y_{t}^{g}|y_{1}^{g},...y_{t-1}^{g},x)
$$
即将 sample 得到的结果当做 baseline,根据 greedy 得到的结果对来计算梯度。
## 使用说明
参考程序[**RLSeq2Seq**](https://github.com/yaserkl/RLSeq2Seq) 和 [**pointer-generator**](https://github.com/abisee/pointer-generator),他们介绍的很清楚,只是这里只用在比赛中,生成的标题较短,且数据集来自官方。
数据的预处理参考 cnn-dailymail 中的 [**make_datafiles.ipynb**](https://github.com/taoyafan/cnn-dailymail/tree/master/bytecup)。
### 文件说明
[src](https://github.com/taoyafan/abstractive_summarization/tree/master/src) 中为源代码,其中[run_summarization.py](https://github.com/taoyafan/abstractive_summarization/blob/master/src/run_summarization.py)为主程序。
[results](https://github.com/taoyafan/abstractive_summarization/tree/master/results)中为不同模型的运行命令(参数)。
### 参数说明
基本命令同[**RLSeq2Seq**](https://github.com/yaserkl/RLSeq2Seq),增加参数如下:
| 参数 | 说明 |
| -------------------------- | ------------------------------------------------------------ |
| convert_version_old_to_new | 为 True 时可加载 [**pointer-generator**](https://github.com/abisee/pointer-generator) 提供的预训练模型 |
| eval_data_path | 验证集路径 |
| dropout_keep_p | 在 encoder 和 decoder 的 LSTM 的 cell 中增加 drop out,对 input、output 和 state 使用相同的 keep_p,默认为1,即不使用 drop out |
| rising_greedy_r | 为 True 时 policy gradient 使用更改后的公式,即目标为提升 greedy 得到的 reward,为 False 时为原公式,但是占用显存增大一倍 |
### 运行说明
在 results 中寻找对应模型的命令,如基准模型 [base_eta=0_lr=0.15.txt](https://github.com/taoyafan/abstractive_summarization/blob/master/results/base_eta%3D0_lr%3D0.15.txt)
在训练时执行命令:
```
python3 run_summarization.py --mode=train --data_path=../finished_files/chunked/train* --eval_data_path=../finished_files/chunked/test* --vocab_path=../finished_files/vocab --log_root=../log --exp_name=base_eta=0_lr=0.15 --batch_size=20 --use_temporal_attention=False --intradecoder=False --eta=0 --rl_training=True --lr=0.15 --sampling_probability=0 --fixed_eta=True --scheduled_sampling=True --fixed_sampling_probability=True --greedy_scheduled_sampling=True
```
经过实验 drop out 取 0.8 时效果最好,不过最终没来得及使用,最终成绩所使用的模型为:
基础模型(有pointer_gen,无coverage,无rl),然后使用 policy gradient。
没有合适的资源?快使用搜索试试~ 我知道了~
Byte Cup 2018国际机器学习竞赛 23 名(水滴队)代码.zip
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