diff --git a/opencompass/configs/summarizers/bbeh.py b/opencompass/configs/summarizers/bbeh.py
new file mode 100644
index 00000000..ba469f82
--- /dev/null
+++ b/opencompass/configs/summarizers/bbeh.py
@@ -0,0 +1,30 @@
+from mmengine.config import read_base
+
+with read_base():
+    from .groups.bbeh import bbeh_summary_groups
+
+# Get all the BBEH subset names from the imported bbeh_summary_groups
+bbeh_subsets = []
+for group in bbeh_summary_groups:
+    if group['name'] == 'bbeh':
+        bbeh_subsets = group['subsets']
+        break
+
+summarizer = dict(
+    # Include both individual datasets and the summary metrics we want to see
+    dataset_abbrs=bbeh_subsets + ['bbeh_naive_average'] + ['bbeh_harmonic_mean'],
+    
+    # Define the summary group for bbeh
+    summary_groups=[
+        {
+            'name': 'bbeh_naive_average',
+            'subsets': bbeh_subsets,
+            'metric': 'naive_average'  # Explicitly specify the metric to use
+        },
+        {
+            'name': 'bbeh_harmonic_mean',
+            'subsets': bbeh_subsets,
+            'metric': 'harmonic_mean'
+        }
+    ]
+)
\ No newline at end of file
diff --git a/opencompass/summarizers/default.py b/opencompass/summarizers/default.py
index 8a0da5b2..f1094f14 100644
--- a/opencompass/summarizers/default.py
+++ b/opencompass/summarizers/default.py
@@ -171,6 +171,8 @@ class DefaultSummarizer:
                         default_metric = 'sum'
                     elif sg.get('weights', []):
                         default_metric = 'weighted_average'
+                    elif sg.get('harmonic_mean', False):
+                        default_metric = 'harmonic_mean'
                     else:
                         default_metric = 'naive_average'
 
@@ -204,6 +206,17 @@ class DefaultSummarizer:
                         avg = sum(scores[metric].values()) / len(scores[metric])
                         variance = sum((scores[metric][k] - avg) ** 2 for k in scores[metric]) / len(scores[metric])
                         scores[metric] = result[metric] = math.sqrt(variance)
+                    elif default_metric == 'harmonic_mean':
+                        # Check for non-positive values that would cause issues in harmonic mean
+                        if any(scores[metric][k] <= 0 for k in scores[metric]):
+                            self.logger.warning(f'Non-positive values found when calculating harmonic mean for {sg["name"]}')
+                            # Handle non-positive values (either skip or use a small positive value)
+                            numerator = len(scores[metric])
+                            denominator = sum(1 / max(scores[metric][k], 1) for k in scores[metric])
+                        else:
+                            numerator = len(scores[metric])
+                            denominator = sum(1 / scores[metric][k] for k in scores[metric])
+                        scores[metric] = result[metric] = numerator / denominator
                     else:
                         if sg.get('weights', []):
                             # check sg['weights'][k] != 0 in case of scores[metric][k] is NaN