diff --git a/nlp-service/extractor.py b/nlp-service/extractor.py
index 19543af5..5d9b230b 100644
--- a/nlp-service/extractor.py
+++ b/nlp-service/extractor.py
@@ -168,3 +168,30 @@ def extract_dates(doc, lang: str) -> tuple[str | None, str | None]:
         return d.isoformat(), None
     # Bare year/date — closed year-range
     return d.isoformat(), _year_end(d).isoformat()
+
+
+# ── Step 4: Keyword extraction ───────────────────────────────────────────────
+
+def extract_keywords(doc, excluded_spans: list) -> list[str]:
+    """Return lowercased lemmas of content words not inside any NER span."""
+    excluded_indices: set[int] = set()
+    for span in excluded_spans:
+        excluded_indices.update(range(span.start, span.end))
+
+    seen: set[str] = set()
+    keywords: list[str] = []
+    for token in doc:
+        if token.i in excluded_indices:
+            continue
+        if token.pos_ not in ("NOUN", "PROPN"):
+            continue
+        if token.is_stop:
+            continue
+        lemma = token.lemma_.lower()
+        if len(lemma) < 3:
+            continue
+        if lemma not in seen:
+            seen.add(lemma)
+            keywords.append(lemma)
+
+    return keywords
diff --git a/nlp-service/test_extractor.py b/nlp-service/test_extractor.py
index b79d188c..8d623178 100644
--- a/nlp-service/test_extractor.py
+++ b/nlp-service/test_extractor.py
@@ -251,3 +251,50 @@ def test_date_after_english(nlp_en):
     date_from, date_to = extract_dates(doc, "en")
     assert date_from == "1900-01-01"
     assert date_to is None
+
+
+# ── Keyword extraction ───────────────────────────────────────────────────────
+
+def test_keywords_extracts_nouns(nlp_de):
+    from extractor import extract_keywords
+    # Use real NLP for POS tags; disable NER to avoid interference
+    doc = nlp_de("Briefe aus dem Krieg", disable=["ner"])
+    keywords = extract_keywords(doc, [])
+    # "Brief" (NOUN) and "Krieg" (NOUN) should appear as lemmas
+    assert "brief" in keywords
+    assert "krieg" in keywords
+
+
+def test_keywords_excludes_stopwords(nlp_de):
+    from extractor import extract_keywords
+    doc = nlp_de("Briefe aus dem Krieg", disable=["ner"])
+    keywords = extract_keywords(doc, [])
+    # "dem" is a stopword article — must not appear
+    assert "dem" not in keywords
+
+
+def test_keywords_excludes_per_ner_spans(nlp_de):
+    from extractor import extract_keywords
+    # Run full NLP for POS tags, then inject a PER span over "Hermann"
+    # "Briefe von Hermann": B=0..6, ' '=6, v=7..10, ' '=10, H=11..18
+    doc = nlp_de("Briefe von Hermann")
+    per_span = doc.char_span(11, 18, label="PER")
+    if per_span:
+        doc.ents = [per_span]
+        keywords = extract_keywords(doc, list(doc.ents))
+        assert "hermann" not in keywords
+
+
+def test_keywords_excludes_short_lemmas(nlp_de):
+    from extractor import extract_keywords
+    doc = nlp_de("Briefe an ihn", disable=["ner"])
+    keywords = extract_keywords(doc, [])
+    # "ihn" is 3 chars but is a stopword pronoun; "an" is 2 chars
+    assert "an" not in keywords
+
+
+def test_keywords_deduplicates(nlp_de):
+    from extractor import extract_keywords
+    doc = nlp_de("Brief Brief Krieg", disable=["ner"])
+    keywords = extract_keywords(doc, [])
+    assert keywords.count("brief") == 1