I used code in the run_eval.py

ROUGE_KEYS = [“rouge1”, “rouge2”, “rougeL”]
scorer = rouge_scorer.RougeScorer(ROUGE_KEYS, use_stemmer=True)
aggregator = scoring.BootstrapAggregator()
for reference_ln, output_ln in zip(context_sum, highlights):
scores = scorer.score(reference_ln, output_ln)
aggregator.add_scores(scores)
result = aggregator.aggregate()
t =  {k: v.mid.fmeasure for k, v in result.items()}

By changing module and  changing from average to mid_measure, the score is getting closer. Though rougeL is worse

{‘rouge1’: 0.4372446280018615,
‘rouge2’: 0.2083375655890657,
‘rougeL’: 0.30426740583778056}