Skip to content

ADR-055: Adaptive Summarization Routing

  • Status: Proposed
  • Date: 2026-02-05
  • Authors: Podcast Scraper Team
  • Related RFCs: RFC-053
  • Related PRDs: PRD-005

Context & Problem Statement

The current summarization pipeline uses a uniform approach for all episodes: BART/LED models with MAP-REDUCE summarization, complex chunking logic, and two-pass aggregation. However, podcasts vary significantly:

  • Duration: 10 minutes to multiple hours
  • Structure: Monologue vs dialogue vs panel discussions
  • Content: Technical vs abstract vs narrative
  • Speaker patterns: Single host, interview format, roundtable discussions

A single summarization strategy does not generalize well:

  • Short episodes (< 15 min) don't need complex chunking
  • Dialogue-heavy episodes benefit from speaker-aware processing
  • Technical content requires extraction-first strategies
  • Long monologues need hierarchical chunking with strong reducers

Decision

We adopt Adaptive Summarization Routing using rule-based routing with episode profiling.

  1. Episode Profiling: Analyzes episode characteristics (duration, speaker count, turn-taking rate, entity density, numeric density, topic drift).
  2. Rule-Based Routing: Deterministic rules route episodes to appropriate summarization strategies (short monologue, short dialogue, technical, long monologue, long dialogue, standard).
  3. Strategy Selection: Each strategy uses appropriate models and processing (e.g., single-pass for short episodes, extraction-first for technical content).
  4. Extraction-First Artifacts: All strategies produce structured intermediate outputs before reduction.

Rationale

  • Optimization: Different episode types benefit from different strategies, improving quality and efficiency
  • Deterministic: Rule-based routing is debuggable and logged for each episode
  • Flexibility: Can add new strategies without changing existing ones
  • Quality: Episode-specific strategies produce better summaries than one-size-fits-all approach
  • Compatibility: Works with existing summarization architecture (RFC-042 hybrid pipeline)

Alternatives Considered

  1. One-Size-Fits-All: Rejected as it misses optimization opportunities and produces inconsistent quality.
  2. Machine Learning Routing: Rejected as it adds complexity and makes routing non-deterministic.
  3. Manual Strategy Selection: Rejected as it requires user input and doesn't scale.

Consequences

  • Positive:
  • Better summary quality across diverse episode types
  • More efficient processing (no unnecessary chunking for short episodes)
  • Deterministic and debuggable routing
  • Easy to add new strategies
  • Negative:
  • Initial implementation complexity
  • Requires episode profiling (adds processing overhead)
  • Neutral:
  • Requires implementation of RFC-053

Implementation Notes

  • Module: src/podcast_scraper/workflow/metadata_generation.py - Summarization pipeline
  • Pattern: Rule-based routing with episode profiling
  • Routing Thresholds:
  • Token count < 2000 → Single-pass strategy
  • Speaker turn rate > 2.0 turns/min → Dialogue strategy
  • Entity density > 10.0 per 1000 tokens → Technical strategy
  • Duration > 60 min + multiple speakers → Panel strategy
  • Episode Profile: Duration, speaker count, turn-taking rate, entity density, numeric density, topic drift
  • Strategies: Short monologue, short dialogue, technical, long monologue, long dialogue, standard
  • Model Roles: Extractor, Summarizer, Reducer, Finalizer (compatible with RFC-042)
  • Status: 🟡 Draft RFC (RFC-053) - Not yet implemented

References