diff --git a/src/psycop_feature_generation/data_checks/flattened/feature_describer.py b/src/psycop_feature_generation/data_checks/flattened/feature_describer.py
index 3ee290a1..b4ef45d8 100644
--- a/src/psycop_feature_generation/data_checks/flattened/feature_describer.py
+++ b/src/psycop_feature_generation/data_checks/flattened/feature_describer.py
@@ -2,6 +2,7 @@
 df."""
 from __future__ import annotations
 
+import re
 from collections.abc import Sequence
 from pathlib import Path
 
@@ -15,7 +16,7 @@
     PredictorSpec,
     StaticSpec,
     TemporalSpec,
-    _AnySpec,
+    TextPredictorSpec,
 )
 from wasabi import Printer
 
@@ -86,16 +87,22 @@ def create_unicode_hist(series: pd.Series) -> pd.Series:
         [UNICODE_HIST[_find_nearest(key_vector, val)] for val in hist],
     )
 
-    return ucode_to_print
+    return pd.Series(ucode_to_print)
 
 
 def generate_temporal_feature_description(
     series: pd.Series,
     predictor_spec: TemporalSpec,
+    feature_name: str | None = None,
 ):
     """Generate a row with feature description for a temporal predictor."""
+    if feature_name is not None:
+        feature_name = feature_name
+    else:
+        feature_name = predictor_spec.feature_name
+
     d = {
-        "Predictor df": predictor_spec.feature_name,
+        "Predictor df": feature_name,
         "Lookbehind days": predictor_spec.interval_days,
         "Resolve multiple": predictor_spec.resolve_multiple_fn.__name__,
         "N unique": series.nunique(),
@@ -133,13 +140,15 @@ def generate_static_feature_description(series: pd.Series, predictor_spec: Stati
 
 def generate_feature_description_row(
     series: pd.Series,
-    predictor_spec: _AnySpec,
+    predictor_spec: StaticSpec | TemporalSpec,
+    feature_name: str | None = None,
 ) -> dict:
     """Generate a row with feature description.
 
     Args:
         series (pd.Series): Series with data to describe.
         predictor_spec (PredictorSpec): Predictor specification.
+        feature_name (str, optional): Name of the feature. Defaults to None.
 
     Returns:
         dict: dictionary with feature description.
@@ -148,20 +157,23 @@ def generate_feature_description_row(
     if isinstance(predictor_spec, StaticSpec):
         d = generate_static_feature_description(series, predictor_spec)
     elif isinstance(predictor_spec, TemporalSpec):
-        d = generate_temporal_feature_description(series, predictor_spec)
-
+        d = generate_temporal_feature_description(
+            series,
+            predictor_spec,
+            feature_name=feature_name,
+        )
     return d
 
 
 def generate_feature_description_df(
     df: pd.DataFrame,
-    predictor_specs: list[PredictorSpec],
+    predictor_specs: list[PredictorSpec | StaticSpec | TemporalSpec],
 ) -> pd.DataFrame:
     """Generate a data frame with feature descriptions.
 
     Args:
         df (pd.DataFrame): Data frame with data to describe.
-        predictor_specs (PredictorSpec): Predictor specifications.
+        predictor_specs (Union[PredictorSpec, StaticSpec, TemporalSpec]): Predictor specifications.
 
     Returns:
         pd.DataFrame: Data frame with feature descriptions.
@@ -172,12 +184,33 @@ def generate_feature_description_df(
     for spec in predictor_specs:
         column_name = spec.get_col_str()
 
-        rows.append(
-            generate_feature_description_row(
-                series=df[column_name],
-                predictor_spec=spec,
-            ),
-        )
+        if isinstance(spec, TextPredictorSpec):
+            last_part = column_name.split(f"{spec.prefix}_{spec.feature_name}")[1]
+            first_part = column_name.split(last_part)[0]
+            string_match = f"{first_part}[\\dA-Za-z\\-]+{last_part}"
+
+            column_names = [
+                re.match(string_match, column)[0]  # type: ignore
+                for column in df.columns
+                if re.match(string_match, column) is not None
+            ]
+
+            for column_name in column_names:
+                rows.append(
+                    generate_feature_description_row(
+                        series=df[column_name],
+                        predictor_spec=spec,
+                        feature_name=column_name,
+                    ),
+                )
+
+        else:
+            rows.append(
+                generate_feature_description_row(
+                    series=df[column_name],
+                    predictor_spec=spec,
+                ),
+            )
 
     # Convert to dataframe
     feature_description_df = pd.DataFrame(rows)
@@ -235,7 +268,7 @@ def save_feature_descriptive_stats_from_dir(
         )
         # Writing html table as well
         save_df_to_pretty_html_table(
+            df=feature_descriptive_stats,
             path=out_dir / f"{split}_feature_descriptive_stats.html",
             title="Feature descriptive stats",
-            df=feature_descriptive_stats,
         )