diff --git a/langchain/text_splitter.py b/langchain/text_splitter.py
index 0cbe9e4ed8..37de47f0fc 100644
--- a/langchain/text_splitter.py
+++ b/langchain/text_splitter.py
@@ -34,6 +34,23 @@ logger = logging.getLogger(__name__)
 TS = TypeVar("TS", bound="TextSplitter")
 
 
+def _make_spacy_pipeline_for_splitting(pipeline: str) -> Any:  # avoid importing spacy
+    try:
+        import spacy
+    except ImportError:
+        raise ImportError(
+            "Spacy is not installed, please install it with `pip install spacy`."
+        )
+    if pipeline == "sentencizer":
+        from spacy.lang.en import English
+
+        sentencizer = English()
+        sentencizer.add_pipe("sentencizer")
+    else:
+        sentencizer = spacy.load(pipeline, disable=["ner"])
+    return sentencizer
+
+
 def _split_text_with_regex(
     text: str, separator: str, keep_separator: bool
 ) -> List[str]:
@@ -1010,25 +1027,24 @@ class NLTKTextSplitter(TextSplitter):
 
 
 class SpacyTextSplitter(TextSplitter):
-    """Implementation of splitting text that looks at sentences using Spacy."""
+    """Implementation of splitting text that looks at sentences using Spacy.
+
+
+    Per default, Spacy's `en_core_web_sm` model is used. For a faster, but
+    potentially less accurate splitting, you can use `pipeline='sentencizer'`.
+    """
 
     def __init__(
         self, separator: str = "\n\n", pipeline: str = "en_core_web_sm", **kwargs: Any
     ) -> None:
         """Initialize the spacy text splitter."""
         super().__init__(**kwargs)
-        try:
-            import spacy
-        except ImportError:
-            raise ImportError(
-                "Spacy is not installed, please install it with `pip install spacy`."
-            )
-        self._tokenizer = spacy.load(pipeline)
+        self._tokenizer = _make_spacy_pipeline_for_splitting(pipeline)
         self._separator = separator
 
     def split_text(self, text: str) -> List[str]:
         """Split incoming text and return chunks."""
-        splits = (str(s) for s in self._tokenizer(text).sents)
+        splits = (s.text for s in self._tokenizer(text).sents)
         return self._merge_splits(splits, self._separator)
 
 
diff --git a/tests/integration_tests/test_nlp_text_splitters.py b/tests/integration_tests/test_nlp_text_splitters.py
index 4837fe20ad..0f2809ede4 100644
--- a/tests/integration_tests/test_nlp_text_splitters.py
+++ b/tests/integration_tests/test_nlp_text_splitters.py
@@ -26,11 +26,12 @@ def test_nltk_text_splitter() -> None:
     assert output == expected_output
 
 
-def test_spacy_text_splitter() -> None:
+@pytest.mark.parametrize("pipeline", ["sentencizer", "en_core_web_sm"])
+def test_spacy_text_splitter(pipeline: str) -> None:
     """Test splitting by sentence using Spacy."""
     text = "This is sentence one. And this is sentence two."
     separator = "|||"
-    splitter = SpacyTextSplitter(separator=separator)
+    splitter = SpacyTextSplitter(separator=separator, pipeline=pipeline)
     output = splitter.split_text(text)
     expected_output = [f"This is sentence one.{separator}And this is sentence two."]
     assert output == expected_output