.planning/backlog/semantic-segmentation.md

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+# Semantic Segmentation for SRT Processing
+
+**Status:** Backlog
+**Priority:** Medium
+**Source:** User feedback during v1.5 testing
+
+## Problem
+
+Current `SemanticSegmenter` uses simple heuristic rules:
+- Time gap > 3 seconds
+- Paragraph length > 500 characters
+
+This approach cannot identify **semantic topic transitions**.
+
+### Example Case
+
+Video: BV1MiPSz2ErY (科学透镜 - 5 topics in one video)
+1. 银河系中心的超大黑洞人马座A*其实不是黑洞
+2. 银河系和仙女座星系等构成的本星系群陷入巨大物质薄片
+3. 科学家为世界上97%的建筑生成3D模型
+4. 雷暴的形成
+5. 闪电的形成
+
+Current segmentation mixes these topics together.
+
+## Potential Solutions
+
+### Option 1: Transition Keyword Detection (Simple)
+- Detect common transition phrases: "接下来", "另外", "第二个", etc.
+- **Pros:** Fast, no API calls
+- **Cons:** Low accuracy, many videos lack explicit transitions
+
+### Option 2: LLM-Based Segmentation (Accurate)
+- Ask LLM to analyze entire subtitle and identify topic boundaries
+- **Pros:** Highest accuracy, understands semantics
+- **Cons:** Extra API cost, token limits for long videos
+
+### Option 3: Two-Stage Approach (Balanced)
+- Stage 1: Simple rule-based segmentation (current)
+- Stage 2: LLM checks paragraph beginnings for new topics
+- **Pros:** Most cases use simple rules, LLM only for key decisions
+- **Cons:** More complex implementation
+
+### Option 4: Embedding-Based Similarity
+- Calculate semantic similarity between adjacent paragraphs
+- Low similarity = topic boundary
+- **Pros:** No LLM API calls, runs locally
+- **Cons:** Requires embedding model, implementation complexity
+
+## Recommendation
+
+**Phase 1 (Quick Win):** Transition keyword detection + improve prompt
+
+**Phase 2 (Full Solution):** Two-stage approach with optional LLM semantic segmentation
+
+## Success Criteria
+
+1. Topics are separated correctly
+2. Each paragraph covers a single coherent topic
+3. No false splits within a topic