class_docstr: 对 **CNN/Daily Mail** 数据进行适用于 ``extractive summarization task`` 的预处理，预处理之后的数据具备以下结构：
  
  .. csv-table::
     :header: "text", "summary", "label", "publication", "text_wd", "words", "seq_len", "target"
  
      "['I got new tires from them and... ','...']", "['The new tires...','...']", "[0, 1]", "cnndm", "[['I','got',...'.'],...,['...']]", "[[54,89,...,5],...,[9,43,..,0]]", "[1,1,...,0]", "[0,1,...,0]"
      "['Don't waste your time.  We had two...','...']", "['Time is precious','...']", "[1]", "cnndm", "[['Don't','waste',...,'.'],...,['...']]", "[[5234,653,...,5],...,[87,234,..,0]]", "[1,1,...,0]", "[1,1,...,0]"
      "['...']", "['...']", "[]", "cnndm", "[[''],...,['']]", "[[],...,[]]", "[]", "[]"
  
  :param vocab_size: 词表大小
  :param sent_max_len: 句子最大长度，不足的句子将 padding ，超出的将截断
  :param doc_max_timesteps: 文章最多句子个数，不足的将 padding，超出的将截断
  :param vocab_path: 外部词表路径
  :param domain: 是否需要建立 domain 词表
  :param num_proc: 处理数据时使用的进程数目。
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code: def __init__(
      self,
      vocab_size: int,
      sent_max_len: int,
      doc_max_timesteps: int,
      vocab_path=None,
      domain=False,
      num_proc=0,
  ):
      self.vocab_size = vocab_size
      self.vocab_path = vocab_path
      self.sent_max_len = sent_max_len
      self.doc_max_timesteps = doc_max_timesteps
      self.domain = domain
      self.num_proc = num_proc
  
imports: import os
  import numpy
  from functools import partial
  from .pipe import Pipe
  from .utils import _drop_empty_instance
  from ..loader.summarization import ExtCNNDMLoader
  from ..data_bundle import DataBundle
  from ...core.vocabulary import Vocabulary
label_desc: Use image node_id 1 to create a new ExtCNNDMPipe object from inherited base classes: Pipe with example: obj = ExtCNNDMPipe(vocab_size, sent_max_len, doc_max_timesteps, vocab_path, domain, num_proc)