diff --git a/bertopic/_bertopic.py b/bertopic/_bertopic.py
index 822586a0..568894d2 100644
--- a/bertopic/_bertopic.py
+++ b/bertopic/_bertopic.py
@@ -3241,37 +3241,37 @@ def merge_models(cls, models, min_similarity: float = .7, embedding_model=None):
 
             # Merge Topic Representations
             new_topics_dict = {}
-            new_topic_val = max_topic + 1
-            for index, new_topic in enumerate(new_topics):
-                new_topic_val = max_topic + index + 1
-                new_topics_dict[new_topic] = new_topic_val
-                merged_topics["topic_representations"][str(new_topic_val)] = selected_topics["topic_representations"][str(new_topic)]
-                merged_topics["topic_labels"][str(new_topic_val)] = selected_topics["topic_labels"][str(new_topic)]
-
-                # Add new aspects
-                if selected_topics["topic_aspects"]:
-                    aspects_1 = set(merged_topics["topic_aspects"].keys())
-                    aspects_2 = set(selected_topics["topic_aspects"].keys())
-                    aspects_diff = aspects_2.difference(aspects_1)
-                    if aspects_diff:
-                        for aspect in aspects_diff:
-                            merged_topics["topic_aspects"][aspect] = {}
-
-                    # If the original model does not have topic aspects but the to be added model does
-                    if not merged_topics.get("topic_aspects"):
-                        merged_topics["topic_aspects"] = selected_topics["topic_aspects"]
-
-                    # If they both contain topic aspects, add to the existing set of aspects
-                    else:
-                        for aspect, values in selected_topics["topic_aspects"].items():
-                            merged_topics["topic_aspects"][aspect][str(new_topic_val)] = values[str(new_topic)]
+            for new_topic in new_topics:
+                if new_topic != -1:
+                    max_topic += 1
+                    new_topics_dict[new_topic] = max_topic
+                    merged_topics["topic_representations"][str(max_topic)] = selected_topics["topic_representations"][str(new_topic)]
+                    merged_topics["topic_labels"][str(max_topic)] = selected_topics["topic_labels"][str(new_topic)]
+
+                    # Add new aspects
+                    if selected_topics["topic_aspects"]:
+                        aspects_1 = set(merged_topics["topic_aspects"].keys())
+                        aspects_2 = set(selected_topics["topic_aspects"].keys())
+                        aspects_diff = aspects_2.difference(aspects_1)
+                        if aspects_diff:
+                            for aspect in aspects_diff:
+                                merged_topics["topic_aspects"][aspect] = {}
+
+                        # If the original model does not have topic aspects but the to be added model does
+                        if not merged_topics.get("topic_aspects"):
+                            merged_topics["topic_aspects"] = selected_topics["topic_aspects"]
+
+                        # If they both contain topic aspects, add to the existing set of aspects
+                        else:
+                            for aspect, values in selected_topics["topic_aspects"].items():
+                                merged_topics["topic_aspects"][aspect][str(max_topic)] = values[str(new_topic)]
 
-                # Add new embeddings
-                new_tensors = tensors[new_topic + selected_topics["_outliers"]]
-                merged_tensors = np.vstack([merged_tensors, new_tensors])
+                    # Add new embeddings
+                    new_tensors = tensors[new_topic + selected_topics["_outliers"]]
+                    merged_tensors = np.vstack([merged_tensors, new_tensors])
 
             # Topic Mapper
-            merged_topics["topic_mapper"] = TopicMapper(list(range(-1, new_topic_val+1, 1))).mappings_
+            merged_topics["topic_mapper"] = TopicMapper(list(range(-1, max_topic+1, 1))).mappings_
 
             # Find similar topics and re-assign those from the new models
             sims_idx = np.argmax(sim_matrix, axis=1)