diff --git a/nerval/evaluate.py b/nerval/evaluate.py
index 00d9e4343e1bc13098849aa208755edfab156a35..ab11dde34444b3ecbf8fcc3e7c5c694be8b63e76 100644
--- a/nerval/evaluate.py
+++ b/nerval/evaluate.py
@@ -492,7 +492,24 @@ def print_results(scores: dict):
     tt.print(results, header, style=tt.styles.markdown)
 
 
-def run(annotation: str, prediction: str, threshold: int) -> dict:
+def print_result_compact(scores: dict):
+    result = []
+    header = ["tag", "predicted", "matched", "Precision", "Recall", "F1", "Support"]
+    result.append(
+        [
+            "ALl",
+            scores["All"]["predicted"],
+            scores["All"]["matched"],
+            round(scores["All"]["P"], 3),
+            round(scores["All"]["R"], 3),
+            round(scores["All"]["F1"], 3),
+            scores["All"]["Support"],
+        ]
+    )
+    tt.print(result, header, style=tt.styles.markdown)
+
+
+def run(annotation: str, prediction: str, threshold: int, verbose: bool) -> dict:
     """Compute recall and precision for each entity type found in annotation and/or prediction.
 
     Each measure is given at document level, global score is a micro-average across entity types.
@@ -535,12 +552,15 @@ def run(annotation: str, prediction: str, threshold: int) -> dict:
     scores = compute_scores(annot["entity_count"], predict["entity_count"], matches)
 
     # Print results
-    print_results(scores)
+    if verbose:
+        print_results(scores)
+    else:
+        print_result_compact(scores)
 
     return scores
 
 
-def run_multiple(file_csv, folder, threshold):
+def run_multiple(file_csv, folder, threshold, verbose):
     """Run the program for multiple files (correlation indicated in the csv file)"""
     # Read the csv in a list
     with open(file_csv, "r") as read_obj:
@@ -563,7 +583,7 @@ def run_multiple(file_csv, folder, threshold):
 
             if annot and predict:
                 print(os.path.basename(predict))
-                run(annot, predict, threshold)
+                run(annot, predict, threshold, verbose)
                 print()
             else:
                 raise f"No file found for files {annot}, {predict}"
@@ -624,6 +644,12 @@ def main():
         help="Folder containing the bio files referred to in the csv file",
         type=Path,
     )
+    parser.add_argument(
+        "-v",
+        "--verbose",
+        help="Print only the recap if False",
+        action="store_false",
+    )
     args = parser.parse_args()
 
     if args.multiple == 1 or args.multiple == 2:
@@ -633,14 +659,14 @@ def main():
             if not args.csv:
                 raise argparse.ArgumentError(args.folder, "-c must be given if -m is 2")
             if args.folder and args.csv:
-                run_multiple(args.csv, args.folder, args.threshold)
+                run_multiple(args.csv, args.folder, args.threshold, args.verbose)
         if args.multiple == 1:
             if not args.annot:
                 raise argparse.ArgumentError(args.folder, "-a must be given if -m is 1")
             if not args.predict:
                 raise argparse.ArgumentError(args.folder, "-p must be given if -m is 1")
             if args.annot and args.predict:
-                run(args.annot, args.predict, args.threshold)
+                run(args.annot, args.predict, args.threshold, args.verbose)
     else:
         raise argparse.ArgumentTypeError("Value has to be 1 or 2")
 
diff --git a/tests/test_run.py b/tests/test_run.py
index cedbd0d169f8336763927606f744569ef940f1d3..4a6e9d598c758b3eb26e538f62407b5601747256 100644
--- a/tests/test_run.py
+++ b/tests/test_run.py
@@ -66,8 +66,8 @@ expected_scores = {
 @pytest.mark.parametrize(
     "test_input, expected",
     [
-        ((FAKE_ANNOT_BIO, FAKE_PREDICT_BIO, THRESHOLD), expected_scores),
-        ((FAKE_BIO_NESTED, FAKE_BIO_NESTED, THRESHOLD), expected_scores_nested),
+        ((FAKE_ANNOT_BIO, FAKE_PREDICT_BIO, THRESHOLD, True), expected_scores),
+        ((FAKE_BIO_NESTED, FAKE_BIO_NESTED, THRESHOLD, True), expected_scores_nested),
     ],
 )
 def test_run(test_input, expected):