diff --git a/app/models/queue.py b/app/models/queue.py
index 28c19fd..a388272 100644
--- a/app/models/queue.py
+++ b/app/models/queue.py
@@ -14,7 +14,7 @@ class ProcessingQueue(Base):
     id: Mapped[int] = mapped_column(BigInteger, primary_key=True)
     document_id: Mapped[int] = mapped_column(BigInteger, ForeignKey("documents.id"), nullable=False)
     stage: Mapped[str] = mapped_column(
-        Enum("extract", "classify", "embed", "preview", name="process_stage"), nullable=False
+        Enum("extract", "classify", "summarize", "embed", "preview", name="process_stage"), nullable=False
     )
     status: Mapped[str] = mapped_column(
         Enum("pending", "processing", "completed", "failed", name="process_status"),
diff --git a/app/workers/news_collector.py b/app/workers/news_collector.py
index 3e7533e..228b56f 100644
--- a/app/workers/news_collector.py
+++ b/app/workers/news_collector.py
@@ -162,7 +162,8 @@ async def _fetch_rss(session, source: NewsSource) -> int:
         session.add(doc)
         await session.flush()
 
-        # embed만 등록 (classify 불필요 — 소스/분야 이미 확정)
+        # summarize + embed 등록 (classify 불필요)
+        session.add(ProcessingQueue(document_id=doc.id, stage="summarize", status="pending"))
         days_old = (datetime.now(timezone.utc) - pub_dt).days
         if days_old <= 30:
             session.add(ProcessingQueue(document_id=doc.id, stage="embed", status="pending"))
@@ -243,6 +244,7 @@ async def _fetch_api(session, source: NewsSource) -> int:
         session.add(doc)
         await session.flush()
 
+        session.add(ProcessingQueue(document_id=doc.id, stage="summarize", status="pending"))
         days_old = (datetime.now(timezone.utc) - pub_dt).days
         if days_old <= 30:
             session.add(ProcessingQueue(document_id=doc.id, stage="embed", status="pending"))
diff --git a/app/workers/queue_consumer.py b/app/workers/queue_consumer.py
index 05c8637..6a21b93 100644
--- a/app/workers/queue_consumer.py
+++ b/app/workers/queue_consumer.py
@@ -11,7 +11,7 @@ from models.queue import ProcessingQueue
 logger = setup_logger("queue_consumer")
 
 # stage별 배치 크기
-BATCH_SIZE = {"extract": 5, "classify": 3, "embed": 1, "preview": 2}
+BATCH_SIZE = {"extract": 5, "classify": 3, "summarize": 3, "embed": 1, "preview": 2}
 STALE_THRESHOLD_MINUTES = 10
 
 
@@ -65,10 +65,12 @@ async def consume_queue():
     from workers.embed_worker import process as embed_process
     from workers.extract_worker import process as extract_process
     from workers.preview_worker import process as preview_process
+    from workers.summarize_worker import process as summarize_process
 
     workers = {
         "extract": extract_process,
         "classify": classify_process,
+        "summarize": summarize_process,
         "embed": embed_process,
         "preview": preview_process,
     }
diff --git a/app/workers/summarize_worker.py b/app/workers/summarize_worker.py
new file mode 100644
index 0000000..848f8e4
--- /dev/null
+++ b/app/workers/summarize_worker.py
@@ -0,0 +1,35 @@
+"""요약 전용 워커 — 뉴스 등 classify 불필요한 문서의 AI 요약만 생성"""
+
+from datetime import datetime, timezone
+
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from ai.client import AIClient, strip_thinking
+from core.utils import setup_logger
+from models.document import Document
+
+logger = setup_logger("summarize_worker")
+
+
+async def process(document_id: int, session: AsyncSession) -> None:
+    """문서 AI 요약 생성 (분류 없이 요약만)"""
+    doc = await session.get(Document, document_id)
+    if not doc:
+        raise ValueError(f"문서 ID {document_id}를 찾을 수 없음")
+
+    if not doc.extracted_text:
+        raise ValueError(f"문서 ID {document_id}: extracted_text가 비어있음")
+
+    if doc.ai_summary:
+        logger.info(f"[요약] document_id={document_id}: 이미 요약 있음, skip")
+        return
+
+    client = AIClient()
+    try:
+        summary = await client.summarize(doc.extracted_text[:15000])
+        doc.ai_summary = strip_thinking(summary)
+        doc.ai_model_version = "qwen3.5-35b-a3b"
+        doc.ai_processed_at = datetime.now(timezone.utc)
+        logger.info(f"[요약] document_id={document_id}: {len(doc.ai_summary)}자")
+    finally:
+        await client.close()