Daily article(5)


                                                                                    Low-carbon life 


    There is no denying the fact that low-carbon life is attracting growing attention and is being well welcomed by people recently.


Part of the explanations for this phenomenon is that it is of vital importance for the whole world to practice the low-carbon lifestyle,


as it enables future generations to live a better life.


    Low-carbon life means a kind of lifestyle in which people do their best to save energy and reduce carbon greenhouse gas


emissions. Firstly, it is absolutely neccessary for us to save and protect our natural resources and energy which are by no means


free from exhaustion. Secondly, in order to protect our environment from serious greenhouse effect, we need a low-carbon


emission.


    What we should bear in mind is that everyone should do one's bit to protect the earth by practicing low-carbon lifestyle. Firstly,


everyone is expected to cultivate the awareness of saving energy. Then, we should also start from day-to-day activities to live a


low-carbon life. Only by doing so can we create a better world for all human beings.



there is no denying the fact that:无可否认的是

phenomenon:现象

vital importance:及其重要

future generations:子孙后代

carbon greehouse gas emissions:碳化温室气体排放

absolutely:adv. 绝对地,完全地

by no means:绝不

exhaustion:n. 枯竭

bear in mind:记住

do one's bit:尽本分

cultivate:培养


<think>嗯,用户想查找DailyMail数据集相关的资源或使用说明。我需要仔细查看提供的引用信息中的相关部分。从引用[1]来看,abisee/cnn-dailymail这个GitHub仓库应该保存着非匿名版本的CNN/DailyMail数据集处理代码[^1]。引用[2]提供了数据集的具体处理方法,包括解压.tgz文件并将.story文件整理到统一目录下的操作步骤[^2]。引用[4]特别提到这个仓库包含Pointer-GeneratorNetworks模型实现,这个模型在文本摘要领域很知名[^4]。用户可能需要完整的下载地址。引用[5]整理的大型数据集网站selectdataset.com中包含该数据集信息[^5],我会在回复时提供完整网址。对于处理步骤,引用[2]中的解压和文件移动命令很重要,特别是需要提醒用户注意整理.story文件。考虑到用户可能需要更详细指导,我应该补充常见的预处理步骤,比如将故事和摘要分离。引用[3]提到这个数据集主要应用于抽象式文本摘要任务[^3],可以简要说明数据集价值。最后需要准备相关问题来帮助用户深入探索,比如处理过程中可能遇到的常见问题和替代资源获取渠道。</think> 基于您对Daily Mail数据集的需求,结合相关引用信息,整理关键资源及使用步骤: ### 数据集获取与使用指南 1. **官方GitHub仓库**(含代码与数据) 📦 项目地址:`github.com/abisee/cnn-dailymail` → 包含数据集预处理代码和非匿名版本下载链接[^1][^4] 2. **预处理流程** ```bash # 解压数据集(示例) tar zxvf cnn_stories.tgz -C raw_stories/ tar zxvf dailymail_stories.tgz -C raw_stories/ ``` → 需合并所有`.story`文件至统一目录[^2] → 文件移动方法参考:[Linux移动命令教程](https://blog.youkuaiyun.com/tailonh/article/details/124301984) 3. **备用下载源** 🌐 集成数据集平台: `https://www.selectdataset.com/dataset/70607709831f83178eb08689ca7accc4`[^5] → 提供444个大型数据集聚合索引 4. **核心应用场景** 💡 抽象文本摘要(Abstractive Summarization) → 支持Pointer-Generator等先进NLP模型训练[^3][^4] ### 典型工作流 ```mermaid graph LR A[下载.tgz文件] --> B[解压至raw_stories] B --> C[合并.story文件] C --> D[数据清洗] D --> E[划分训练/验证集] E --> F[模型训练] ``` ###
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