code: def rank_reference_paragraphs(wiki_title, references_content, normalize=True):
    
    normalized_title = _normalize_text(wiki_title)
    title_tokens = _tokens_to_score(
        set(tokenizer.encode(text_encoder.native_to_unicode(normalized_title))))
    ref_paragraph_info = []
    doc_counts = collections.defaultdict(int)
    for ref in references_content:
      for paragraph in ref.split("\n"):
        normalized_paragraph = _normalize_text(paragraph)
        if cc_utils.filter_paragraph(normalized_paragraph):
          # Skip paragraph
          continue
        counts = _token_counts(normalized_paragraph, title_tokens)
        for token in title_tokens:
          if counts[token]:
            doc_counts[token] += 1
        content = normalized_paragraph if normalize else paragraph
        info = {"content": content, "counts": counts}
        ref_paragraph_info.append(info)
  
    for info in ref_paragraph_info:
      score = 0.
      for token in title_tokens:
        term_frequency = info["counts"][token]
        inv_doc_frequency = (
            float(len(ref_paragraph_info)) / max(doc_counts[token], 1))
        score += term_frequency * math.log(inv_doc_frequency)
      info["score"] = score
  
    ref_paragraph_info.sort(key=lambda el: el["score"], reverse=True)
    return [info["content"] for info in ref_paragraph_info]
docs: Rank and return reference paragraphs by tf-idf score on title tokens.