diff --git a/bertopic/_bertopic.py b/bertopic/_bertopic.py
index 90379b86..6ae842e2 100644
--- a/bertopic/_bertopic.py
+++ b/bertopic/_bertopic.py
@@ -370,6 +370,7 @@ def fit_transform(
         embeddings: np.ndarray = None,
         images: List[str] = None,
         y: Union[List[int], np.ndarray] = None,
+        nr_repr_docs: int = None,
     ) -> Tuple[List[int], Union[np.ndarray, None]]:
         """Fit the models on a collection of documents, generate topics,
         and return the probabilities and topic per document.
@@ -381,6 +382,7 @@ def fit_transform(
             images: A list of paths to the images to fit on or the images themselves
             y: The target class for (semi)-supervised modeling. Use -1 if no class for a
                specific instance is specified.
+            nr_repr_docs: [optional] Number of representative docs to keep for each topic.
 
         Returns:
             predictions: Topic predictions for each documents
@@ -487,7 +489,7 @@ def fit_transform(
                 custom_documents = self._reduce_topics(custom_documents)
 
             # Save the top 3 most representative documents per topic
-            self._save_representative_docs(custom_documents)
+            self._save_representative_docs(custom_documents, nr_repr_docs=nr_repr_docs)
         else:
             # Extract topics by calculating c-TF-IDF
             self._extract_topics(documents, embeddings=embeddings, verbose=self.verbose)
@@ -497,7 +499,7 @@ def fit_transform(
                 documents = self._reduce_topics(documents)
 
             # Save the top 3 most representative documents per topic
-            self._save_representative_docs(documents)
+            self._save_representative_docs(documents, nr_repr_docs=nr_repr_docs)
 
         # In the case of zero-shot topics, probability will come from cosine similarity,
         # and the HDBSCAN model will be removed
@@ -2077,6 +2079,7 @@ def merge_topics(
         docs: List[str],
         topics_to_merge: List[Union[Iterable[int], int]],
         images: List[str] = None,
+        nr_repr_docs: int = None,
     ) -> None:
         """Arguments:
             docs: The documents you used when calling either `fit` or `fit_transform`
@@ -2087,6 +2090,7 @@ def merge_topics(
                                 separately merge topics 3 and 4.
             images: A list of paths to the images used when calling either
                     `fit` or `fit_transform`.
+            nr_repr_docs: [optional] Number of representative docs to keep for each topic.
 
         Examples:
         If you want to merge topics 1, 2, and 3:
@@ -2147,7 +2151,7 @@ def merge_topics(
         documents = self._sort_mappings_by_frequency(documents)
         self._extract_topics(documents, mappings=mappings)
         self._update_topic_size(documents)
-        self._save_representative_docs(documents)
+        self._save_representative_docs(documents, nr_repr_docs=nr_repr_docs)
         self.probabilities_ = self._map_probabilities(self.probabilities_)
 
     def reduce_topics(
@@ -2156,6 +2160,7 @@ def reduce_topics(
         nr_topics: Union[int, str] = 20,
         images: List[str] = None,
         use_ctfidf: bool = False,
+        nr_repr_docs: int = None,
     ) -> None:
         """Reduce the number of topics to a fixed number of topics
         or automatically.
@@ -2176,6 +2181,7 @@ def reduce_topics(
                     `fit` or `fit_transform`
             use_ctfidf: Whether to calculate distances between topics based on c-TF-IDF embeddings. If False, the
                         embeddings from the embedding model are used.
+            nr_repr_docs: [optional] Number of representative docs to keep for each topic.
 
         Updates:
             topics_ : Assigns topics to their merged representations.
@@ -2212,7 +2218,7 @@ def reduce_topics(
         # Reduce number of topics
         documents = self._reduce_topics(documents, use_ctfidf)
         self._merged_topics = None
-        self._save_representative_docs(documents)
+        self._save_representative_docs(documents, nr_repr_docs=nr_repr_docs)
         self.probabilities_ = self._map_probabilities(self.probabilities_)
 
         return self
@@ -3993,21 +3999,22 @@ def _extract_topics(
         if verbose:
             logger.info("Representation - Completed \u2713")
 
-    def _save_representative_docs(self, documents: pd.DataFrame):
+    def _save_representative_docs(self, documents: pd.DataFrame, nr_repr_docs: int):
         """Save the 3 most representative docs per topic.
 
         Arguments:
             documents: Dataframe with documents and their corresponding IDs
+            nr_repr_docs: The number of representative documents to extract per topic
 
         Updates:
-            self.representative_docs_: Populate each topic with 3 representative docs
+            self.representative_docs_: Populate each topic with {nr_repr_docs} representative docs
         """
         repr_docs, _, _, _ = self._extract_representative_docs(
             self.c_tf_idf_,
             documents,
             self.topic_representations_,
             nr_samples=500,
-            nr_repr_docs=3,
+            nr_repr_docs=nr_repr_docs,
         )
         self.representative_docs_ = repr_docs