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
  
      Args:
        evaluated_sentences: The sentences that have been picked by the summarizer
        reference_sentences: The sentences from the referene set
  
      Returns:
        A 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_p_r_lcs(lcs, m, n)
  
file_path: nlp-architect/examples/sparse_gnmt/gnmt/utils/rouge.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  import itertools
  import numpy
label_desc: Use image node_id 9 for calling a global function with example usage: rouge_l_sentence_level(evaluated_sentences, reference_sentences) and returns: _f_p_r_lcs