论文代码链接：https://github.com/becxer/pointer-generator/
一、数据（cnn,dailymail）
数据处理（代码链接）：https://github.com/becxer/cnn-dailymail/
把数据集处理成二进制形式
1、下载数据
需翻墙，下载cnn和daily mail的两个stories文件
有的文件包含的例子中的文章缺失了，新代码中把这些去除了。
2、下载Stanford corenlp（现在最新版是3.8.0，但是笔者试了是不行的，必须要用3.7.0版的）
环境：linux
我们需要Stanford corenlp来把数据分词。
把下列这行代码加到你的.bashrc里面(vim .bashrc)
export CLASSPATH=/path/to/stanford-corenlp-full-2016-10-31/stanford-corenlp-3.7.0.jar把/path/to/替换为你保存stanford-corenlp-full-2016-10-31的地方的路径
检测：
运行下列代码：
echo "Please tokenize this text." | java edu.stanford.nlp.process.PTBTokenizer你会看到下列输出：
Please
tokenize
this
text
.
PTBTokenizer tokenized 5 tokens at 68.97 tokens per second.3、Process into .bin and vocab files
运行：
python make_datafiles.py /path/to/cnn/stories /path/to/dailymail/stories把/path/to/cnn/stories替换为你保存cnn/stories文件的路径；dailymail同样
这个脚本做了以下几件事：1、将生成cnn_stories_tokenized和dm_stories_tokenized两个文件夹，里面的数据是已经被分词了的的cnn/stories和dailymail/stories。这可能需要花一些时间。你可能会看到一些来自Stanford Tokenizer “Untokenizable:”的警告，这似乎是跟Unicode character有关。2、对于每一个all_train.txt, all_val.txt and all_test.txt，相应的分词的数据，被小写进二进制文件train.bin, val.bin and test.bin中。同时放在新生产的finished_files文件夹里，这也需要花点时间。3、例外，从训练数据中会生成一个vocab文件，这个文件也被放在finished_files里。4、最后，train.bin, val.bin and test.bin将被分为数据块，每个数据块里有1000个例子。这些数据块文件会被保存在finished_fies/chunked里，例如train_000.bin, train_001.bin, …, train_287.bin。你可以使用单独的文件或者数据块作为模型的输入。（注意事项)
运行结果：
Untokenizable: (U+202A, decimal: 8234)
Untokenizable: ₩ (U+20A9, decimal: 8361)
Untokenizable: ₩ (U+20A9, decimal: 8361)
Untokenizable: (U+202F, decimal: 8239)
Untokenizable: ️ (U+FE0F, decimal: 65039)
Untokenizable: (U+202C, decimal: 8236)
Untokenizable: (U+202A, decimal: 8234)
Untokenizable: ₩ (U+20A9, decimal: 8361)
Untokenizable: (U+202A, decimal: 8234)
Untokenizable: (U+202F, decimal: 8239)
Untokenizable: ₩ (U+20A9, decimal: 8361)
Untokenizable: (U+F06E, decimal: 61550)
Untokenizable: (U+202C, decimal: 8236)
Untokenizable: ₩ (U+20A9, decimal: 8361)
Untokenizable: ₩ (U+20A9, decimal: 8361)
PTBTokenizer tokenized 80044550 tokens at 864874.49 tokens per second.
Stanford CoreNLP Tokenizer has finished.
Successfully finished tokenizing ../cnn/stories to cnn_stories_tokenized.
Preparing to tokenize ../dailymail/stories to dm_stories_tokenized...（同上，省略过程）
......
PTBTokenizer tokenized 203071165 tokens at 916186.85 tokens per second.
Stanford CoreNLP Tokenizer has finished.
Successfully finished tokenizing ../dailymail/stories to dm_stories_tokenized.
Making bin file for URLs listed in url_lists/all_val.txt...
Writing story 0 of 13368; 0.00 percent done
Writing story 1000 of 13368; 7.48 percent done
Writing story 2000 of 13368; 14.96 percent done
Writing story 3000 of 13368; 22.44 percent done
Writing story 4000 of 13368; 29.92 percent done
Writing story 5000 of 13368; 37.40 percent done
Writing story 6000 of 13368; 44.88 percent done
Writing story 7000 of 13368; 52.36 percent done
Writing story 8000 of 13368; 59.84 percent done
Writing story 9000 of 13368; 67.32 percent done
Writing story 10000 of 13368; 74.81 percent done
Writing story 11000 of 13368; 82.29 percent done
Writing story 12000 of 13368; 89.77 percent done
Writing story 13000 of 13368; 97.25 percent done
Finished writing file finished_files/val.bin
Making bin file for URLs listed in url_lists/all_train.txt...（同前两个，省略过程）
......
Writing story 287000 of 287227; 99.92 percent done
Finished writing file finished_files/train.bin
Writing vocab file...
Finished writing vocab file
Splitting train data into chunks...
Splitting val data into chunks...
Splitting test data into chunks...
Saved chunked data in finished_files/chunked补充：
每篇文章用空格隔开每个句子（形式为：‘句子1 句子2 句子3…’） 每个句子里面也是分好词的（PTB）
转载：https://blog.csdn.net/weixin_41267423/article/details/78757273
