def _rouge(ref_file, summarization_file, subword_option=None):
references = []
with codecs.getreader("utf-8")(tf.gfile.GFile(ref_file, "rb")) as f:
for line in f:
references.append(_clean(line, subword_option))
hypotheses = []
with codecs.getreader("utf-8")(tf.gfile.GFile(summarization_file,
"rb")) as f:
for line in f:
hypotheses.append(_clean(line, subword_option=None))
rouge_score_map = rouge.rouge(hypotheses, references)
return 100 * rouge_score_map["rouge_l/f_score"]
def _rouge(ref_file, summarization_file, bpe_delimiter=None):
"""Compute ROUGE scores and handling BPE."""
references = []
with codecs.getreader("utf-8")(tf.gfile.GFile(ref_file, "rb")) as fh:
for line in fh:
references.append(_clean(line, bpe_delimiter))
hypotheses = []
with codecs.getreader("utf-8")(tf.gfile.GFile(summarization_file,
"rb")) as fh:
for line in fh:
hypotheses.append(_clean(line, bpe_delimiter))
rouge_score_map = rouge.rouge(hypotheses, references)
return 100 * rouge_score_map["rouge_l/f_score"]