A courts summarizer must be scored on whether required summary fields appear from docket excerpts. What test-dataset practice helps?

Mapping docket excerpts to required summary fields for structured scoring in the test dataset lets a courts summarizer be graded on completeness, not just prose style. Court summaries often must include parties, hearing dates, charges or motion types, and disposition fields. A structured test dataset pairs each excerpt with the expected field values so automated scoring can check presence and correctness of each required element—aligning GenAIOps evaluation with how clerks and auditors actually use the summaries.

Scoring n-gram overlap of docket excerpts to required summary fields for checks fails operational usefulness; overlap with source text cannot confirm that each required field was correctly extracted and still lets missing fields break clerk workflows. Collecting human fluency ratings on docket excerpts for required summary fields fails structured evaluation—fluency scores do not verify that required fields are present. Testing robustness with redacted docket excerpts for required summary fields fails the field-presence task—tolerance of redaction does not score whether required summary fields appear.

Exam caveat: Structured field scoring must respect sealing, redaction, and PII rules in court data; test datasets need careful anonymization. Field schemas should match the production summary template so eval success predicts clerk-usable output.

Operational check: Confirm the test dataset schema lists required summary fields; score precision/recall or exact-match per field on docket excerpts; gate releases on field-coverage thresholds; review systematic misses with court SMEs; and refresh excerpts when docket formats change.