diff --git a/siuba/dply/verbs.py b/siuba/dply/verbs.py
index 7ece9ad1..e1bdf55a 100644
--- a/siuba/dply/verbs.py
+++ b/siuba/dply/verbs.py
@@ -4,6 +4,7 @@
 import numpy as np
 
 from pandas.core.groupby import DataFrameGroupBy
+from pandas.core.dtypes.inference import is_scalar
 from siuba.siu import Symbolic, Call, strip_symbolic, MetaArg, BinaryOp, create_sym_call, Lazy
 
 DPLY_FUNCTIONS = (
@@ -391,10 +392,15 @@ def summarize(__data, **kwargs):
     for k, v in kwargs.items():
         res = v(__data) if callable(v) else v
 
-        # TODO: validation?
+        # validate operations returned single result
+        if not is_scalar(res) and len(res) > 1:
+            raise ValueError("Summarize argument, %s, must return result of length 1 or a scalar." % k)
 
-        results[k] = res
+        # keep result, but use underlying array to avoid crazy index issues
+        # on DataFrame construction (#138)
+        results[k] = res.array if isinstance(res, pd.Series) else res
         
+    # must pass index, or raises error when using all scalar values
     return DataFrame(results, index = [0])
 
     
diff --git a/siuba/tests/test_verb_summarize.py b/siuba/tests/test_verb_summarize.py
index b645a59b..aaa80633 100644
--- a/siuba/tests/test_verb_summarize.py
+++ b/siuba/tests/test_verb_summarize.py
@@ -99,10 +99,25 @@ def test_summarize_unnamed_args(df):
             )
 
 
-@pytest.mark.skip("TODO: Summarize should fail when result len > 1 (#138)")
+def test_summarize_validates_length():
+    with pytest.raises(ValueError):
+        summarize(data_frame(x = [1,2]), res = _.x + 1)
+
+
 def test_frame_mode_returns_many():
+    # related to length validation above
     with pytest.raises(ValueError):
         df = data_frame(x = [1, 2, 3])
         res = summarize(df, result = _.x.mode())
 
 
+def test_summarize_removes_series_index():
+    # Note: currently wouldn't work in postgresql, since _.x + _.y not an agg func
+    df = data_frame(g = ['a', 'b', 'c'], x = [1,2,3], y = [4,5,6])
+
+    assert_equal_query(
+            df,
+            group_by(_.g) >> summarize(res = _.x + _.y),
+            df.assign(res = df.x + df.y).drop(columns = ["x", "y"])
+            )
+