code: def rouge_l_sentence_level(evaluated_sentences, reference_sentences):
      """
      Computes ROUGE-L (sentence level) of two text collections of sentences.
      http://research.microsoft.com/en-us/um/people/cyl/download/papers/
      rouge-working-note-v1.3.1.pdf
  
      Calculated according to:
      R_lcs = LCS(X,Y)/m
      P_lcs = LCS(X,Y)/n
      F_lcs = ((1 + beta^2)*R_lcs*P_lcs) / (R_lcs + (beta^2) * P_lcs)
  
      where:
      X = reference summary
      Y = Candidate summary
      m = length of reference summary
      n = length of candidate summary
  
      :param evaluated_sentences:
          The sentences that have been picked by the summarizer
      :param reference_sentences:
          The sentences from the reference set
      :returns float: F_lcs
      :raises ValueError: raises exception if a param has len <= 0
      """
      if len(evaluated_sentences) <= 0 or len(reference_sentences) <= 0:
          raise (
              ValueError(
                  "Collections must contain at least 1 sentence."
              )
          )
      reference_words = _split_into_words(reference_sentences)
      evaluated_words = _split_into_words(evaluated_sentences)
      m = len(reference_words)
      n = len(evaluated_words)
      lcs = _len_lcs(evaluated_words, reference_words)
      return _f_lcs(lcs, m, n)
  
file_path: sumy/sumy/evaluation/rouge.py
imports: from __future__ import absolute_import
  from __future__ import division, print_function, unicode_literals
  from ..models.dom import Sentence
label_desc: Use image node_id 12 for calling a global function with example usage: rouge_l_sentence_level(evaluated_sentences, reference_sentences) and returns: _f_lcs