class_docstr: Create a doc instance of text, obtain cleaned, readable text and
  metadata from this doc.
  
  Properties:
  raw: incoming, unedited text
  language: 2-letter code for the language of the text
  is_detected_language: is the language detected or specified beforehand
  is_reliable_language: is the language specified or was it reliably detected
  hint_language: language you expect your text to be
  _spacy_nlps: nested dictionary {lang: {model_id: model}} with loaded spacy language modules
docstr: returns a textrank summary of the document (extractive summary)
  
  >>> from pprint import pprint
  >>> from textpipe.doc import Doc
  >>> text = '''Rice Pudding - Poem by Alan Alexander Milne
  ... What is the matter with Mary Jane?
  ... She's crying with all her might and main,
  ... And she won't eat her dinner - rice pudding again -
  ... What is the matter with Mary Jane?
  ... What is the matter with Mary Jane?
  ... I've promised her dolls and a daisy-chain,
  ... And a book about animals - all in vain -
  ... What is the matter with Mary Jane?
  ... What is the matter with Mary Jane?
  ... She's perfectly well, and she hasn't a pain;
  ... But, look at her, now she's beginning again! -
  ... What is the matter with Mary Jane?
  ... What is the matter with Mary Jane?
  ... I've promised her sweets and a ride in the train,
  ... And I've begged her to stop for a bit and explain -
  ... What is the matter with Mary Jane?
  ... What is the matter with Mary Jane?
  ... She's perfectly well and she hasn't a pain,
  ... And it's lovely rice pudding for dinner again!
  ... What is the matter with Mary Jane?'''
  >>> document = Doc(text)
  >>> pprint(document.summary)
  ["She's crying with all her might and main, And she won't eat her dinner - "
   'rice pudding again - What is the matter with Mary Jane?',
   "She's perfectly well and she hasn't a pain, And it's lovely rice pudding for "
   'dinner again!']
  >>> document = Doc('just 1 sentence.')
  >>> document.summary
  []
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code: def summary(self):
      """
      returns a textrank summary of the document (extractive summary)
  
      >>> from pprint import pprint
      >>> from textpipe.doc import Doc
      >>> text = '''Rice Pudding - Poem by Alan Alexander Milne
      ... What is the matter with Mary Jane?
      ... She's crying with all her might and main,
      ... And she won't eat her dinner - rice pudding again -
      ... What is the matter with Mary Jane?
      ... What is the matter with Mary Jane?
      ... I've promised her dolls and a daisy-chain,
      ... And a book about animals - all in vain -
      ... What is the matter with Mary Jane?
      ... What is the matter with Mary Jane?
      ... She's perfectly well, and she hasn't a pain;
      ... But, look at her, now she's beginning again! -
      ... What is the matter with Mary Jane?
      ... What is the matter with Mary Jane?
      ... I've promised her sweets and a ride in the train,
      ... And I've begged her to stop for a bit and explain -
      ... What is the matter with Mary Jane?
      ... What is the matter with Mary Jane?
      ... She's perfectly well and she hasn't a pain,
      ... And it's lovely rice pudding for dinner again!
      ... What is the matter with Mary Jane?'''
      >>> document = Doc(text)
      >>> pprint(document.summary)
      ["She's crying with all her might and main, And she won't eat her dinner - "
       'rice pudding again - What is the matter with Mary Jane?',
       "She's perfectly well and she hasn't a pain, And it's lovely rice pudding for "
       'dinner again!']
      >>> document = Doc('just 1 sentence.')
      >>> document.summary
      []
      """
      return self.generate_textrank_summary()
  
imports: import functools
  import re
  import unicodedata
  from collections import Counter
  from urllib.parse import urlparse
  import cld2
  import numpy
  import spacy
  import spacy.matcher
  import textacy
  import textacy.ke
  import textacy.text_utils
  from bs4 import BeautifulSoup
  from datasketch import MinHash
  from gensim.models.keyedvectors import KeyedVectors
  from gensim.summarization.summarizer import summarize
  from textpipe.data.emoji import EMOJI_TO_UNICODE_NAME, EMOJI_TO_SENTIMENT
  from textpipe.wrappers import RedisKeyedVectors
  from textpipe.util import getattr_
label_desc: Use image node_id 33 for calling the Doc obj's underlying member method code with example usage: obj.summary() and returns: self