Verify summary claims, corrections and human-review triggers

Last updated: 6 Oct 20265 min read

tutorial

A summary checker should catch unsupported claims, changed quantities and superseded statements, while admitting when automated checks cannot decide.

Build a claim ledger

Represent each sentence as a claim with type, source revision, supporting span and validation status. Numbers, dates, names and causal verbs deserve special attention. Compare extracted values against cited passages, but do not assume identical words prove entailment: “deployment paused” does not mean “deployment failed.” Record a distinct unsupported state rather than silently deleting a claim. The evidence contract defines what each row must carry.

Track correction order

Operational logs contain revisions. A later entry may reverse an earlier cause, change an impact count or close an action. Keep timestamps and authority of each statement, not just the document order. A summary should identify the latest confirmed state and preserve uncertainty where entries conflict. If the source provides no adjudication, route the claim to a reviewer. This is a temporal interpretation problem, not a string-matching problem.

Evaluate the validator itself

Create a reviewed audit set with supported, unsupported, contradictory and unanswerable claims. Include entity swaps, date shifts, negation and misleading quotations. Measure false negatives for harmful unsupported claims and false positives that create unnecessary review work. Split by incident and time. An automated entailment score is a candidate signal, not an oracle; measure calibration by claim type before setting a threshold. Slice evaluation and grouped validation both apply.

Make review actionable

Return a reason and the exact source window for each flagged claim. A reviewer needs to see whether the evidence is missing, contradicted or merely ambiguous. Keep their correction linked to the candidate and source version. Publish a summary only after all mandatory claims pass the chosen policy; otherwise retain a draft or an extractive fallback. Feed reviewed failures into the handoff project, not straight into a training set without label checks.

Implementation

def claim_release_status(claims, required_claim_types):
    seen = set()
    for claim in claims:
        if claim["status"] != "supported":
            return "manual-review"
        seen.add(claim["type"])
    return "publish" if required_claim_types <= seen else "manual-review"

reviewed_claims = [
    {"type": "impact", "status": "supported"},
    {"type": "mitigation", "status": "supported"},
]
assert claim_release_status(reviewed_claims, {"impact", "mitigation"}) == "publish"
assert claim_release_status(reviewed_claims, {"impact", "cause"}) == "manual-review"

Performance and operating cost

The gate scans c claims in O(c) time and stores O(t) claim types for t distinct types. Automated claim verification may require a model call per sentence or batch, which can dominate cost and latency. Batch compatible claims, but keep source references separated so evidence cannot cross documents. Monitor review load, false-negative rate for critical fields and drift in source length; each changes the true operating cost.

Common Mistakes

Using a lexical match as proof of entailment.
Ignoring negation or a later correction in the same source.
Letting an unsupported optional sentence pass because mandatory fields exist.
Reporting validator accuracy without separating costly false negatives.