diff --git a/genienlp/metrics.py b/genienlp/metrics.py
index 63636651..a186c8b3 100644
--- a/genienlp/metrics.py
+++ b/genienlp/metrics.py
@@ -29,7 +29,7 @@
 
 import logging
 from collections import Counter, OrderedDict, defaultdict
-from typing import Iterable, Union
+from typing import List, Union
 
 import sacrebleu
 from datasets import load_metric
@@ -84,6 +84,12 @@ def metric_max_over_ground_truths(metric_fn, prediction, ground_truths):
     return max(scores_for_ground_truths)
 
 
+def computeROUGE(outputs, targets, rouge_types):
+    targets = [target[0] for target in targets]
+    rouge_metric = load_metric('rouge')
+    return rouge_metric.compute(references=targets, predictions=outputs, rouge_types=rouge_types)
+
+
 def computeSequenceClassificationPrecision(outputs, targets):
     targets = [target[0] for target in targets]
     precision_metric = load_metric('precision')
@@ -267,12 +273,12 @@ def compute_ner_f1(predictions, answers, schema='IOB2'):
 
 
 def compute_metrics(
-    predictions: Iterable[str],
-    answers: Union[Iterable[str], Iterable[Iterable[str]]],
-    requested_metrics: Iterable,
+    predictions: List[str],
+    answers: Union[List[str], List[List[str]]],
+    requested_metrics: List,
     lang: str,
     args,
-    example_ids: Iterable[str] = None,
+    example_ids: List[str] = None,
 ):
     """
     Inputs:
@@ -359,6 +365,13 @@ def compute_metrics(
         ner_f1 = compute_ner_f1(predictions, answers)
         metric_keys.append('ner_f1')
         metric_values.append(ner_f1)
+    for m in ['rouge1', 'rouge2', 'rougeL']:
+        if m in requested_metrics:
+            rouge = computeROUGE(predictions, answers, rouge_types=[m])[m]
+            requested_metrics.remove(m)
+            requested_metrics += [f'{m}_low', f'{m}_mid', f'{m}_high']
+            metric_keys += [f'{m}_low', f'{m}_mid', f'{m}_high']
+            metric_values += [rouge.low.fmeasure, rouge.mid.fmeasure, rouge.high.fmeasure]
 
     metric_dict = dict(zip(metric_keys, metric_values))
     metric_dict = OrderedDict((key, metric_dict[key]) for key in requested_metrics)