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Summarization with factual fidelity

Summarization with factual fidelity

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Preserve the source meaning while shortening text.

1. Input boundaries

Respect documented input limits and language support. Split long material using meaningful sections and record what was excluded. Arbitrary truncation can remove the conclusion or a critical exception.

2. Output evaluation

Check whether key facts survive and whether new claims appear. A short, fluent summary may still reverse a negation. Compare source and summary using a factual checklist.

3. User controls

Let the user inspect the original and regenerate or edit the result. Keep generated text distinct from authored notes until the user accepts it.

Worked scenario

A note says a delivery is not confirmed. A summary saying delivery is confirmed fails fidelity even though it is shorter and grammatical.

Apply it

Evaluate notes containing negation, dates, names and contradictory statements. Record omissions separately from invented facts.

Check your understanding

Your quality check catches meaning changes, not only awkward wording. Explain the decision and show evidence from your implementation or design. If you cannot demonstrate it yet, revisit the relevant section before continuing.

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Summarization with factual fidelity | Gemini Nano + ML Kit GenAI | Android Engineers