diff --git a/examples/summarization.ipynb b/examples/summarization.ipynb
index 75e3d0ea..454bbf53 100644
--- a/examples/summarization.ipynb
+++ b/examples/summarization.ipynb
@@ -213,7 +213,7 @@
     "from evaluate import load\n",
     "\n",
     "raw_datasets = load_dataset(\"xsum\")\n",
-    "metric = load(\"rouge\")"
+    "rouge = load(\"rouge\")"
    ]
   },
   {
@@ -409,7 +409,7 @@
     "id": "lnjDIuQ3IrI-"
    },
    "source": [
-    "The metric is an instance of [`datasets.Metric`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Metric):"
+    "The rouge is a metric and is an instance of [`evaluate.Metric`](https://huggingface.co/docs/evaluate/main/en/package_reference/main_classes#evaluate.Metric):"
    ]
   },
   {
@@ -461,7 +461,7 @@
     }
    ],
    "source": [
-    "metric"
+    "rouge"
    ]
   },
   {
@@ -496,7 +496,7 @@
    "source": [
     "fake_preds = [\"hello there\", \"general kenobi\"]\n",
     "fake_labels = [\"hello there\", \"general kenobi\"]\n",
-    "metric.compute(predictions=fake_preds, references=fake_labels)"
+    "rouge.compute(predictions=fake_preds, references=fake_labels)"
    ]
   },
   {
@@ -904,7 +904,7 @@
     "    \n",
     "    # Note that other metrics may not have a `use_aggregator` parameter\n",
     "    # and thus will return a list, computing a metric for each sentence.\n",
-    "    result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True, use_aggregator=True)\n",
+    "    result = rouge.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True, use_aggregator=True)\n",
     "    # Extract a few results\n",
     "    result = {key: value * 100 for key, value in result.items()}\n",
     "    \n",