def METHOD_NAME(stories, lang="english", doc_limit=-1, delimiter=""):
  from nltk.tokenize.treebank import TreebankWordTokenizer
  tokenizer = TreebankWordTokenizer()
  from nltk.corpus import stopwords
  stop = stopwords.words('english')
  from string import ascii_lowercase
  docs = {}
  print("Found %i stories" % stories.count())
  for story in stories:
    text = zlib.decompress(story.story_content_z)
    # text = story.story_title
    text = ''.join(BeautifulSoup(text, features="lxml").findAll(text=True)).lower()
    if delimiter:
      sections = text.split(delimiter)
    else:
      sections = [text]
    if doc_limit > 0 and len(docs) > doc_limit:
      print("Passed doc limit %i" % len(docs))
      break
    print(story.story_title, len(sections))
    for jj in xrange(len(sections)):
      docs["%s-%i" % (story.story_title, jj)] = [x for x in tokenizer.tokenize(sections[jj]) \
                                  if (not x in stop) and \
                                  (min(y in ascii_lowercase for y in x))]
  return docs