Do I need custom metrics for summarization quality?

Not always.

Short answer

For summarization, you usually don’t need custom metrics to start. A combination of general text generation metrics and human evaluation is often enough.

When standard metrics are enough

Use common metrics if you want:

• a quick baseline
• easy comparison between models
• objective, repeatable measurement

Typical choices:

• ROUGE: measures overlap with reference summaries; still the most common baseline
• BERTScore / semantic similarity metrics: better for meaning similarity than exact word overlap
• Length / compression ratio: useful for checking if summaries are too short or too verbose
• Factuality checks: if hallucinations are a concern, use factual consistency metrics or human review

When custom metrics help

You may want custom metrics if your summarization task has special goals, such as:

• domain-specific correctness
  e.g. legal, medical, finance, where missing or wrong details matter a lot
• structured requirements
  e.g. the summary must include sections like “risks,” “actions,” “dates,” or “decisions”
• style constraints
  e.g. executive tone, bullet format, plain language, reading level
• task-specific utility
  e.g. the summary is judged by how well it supports downstream decisions, not just similarity to a reference

Good practical approach

A common evaluation stack is:

1. ROUGE / BERTScore for automatic comparison
2. Human evaluation on:
   • relevance
   • completeness
   • factual accuracy
   • readability
3. Custom metrics only if standard metrics miss what matters

Rule of thumb

• If your summaries are general-purpose: no, custom metrics are probably not necessary
• If your task has strict business/domain goals: yes, custom metrics can be very useful

If you want, I can suggest a metric setup for your specific summarization use case.