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Natural-Language Report Summarization

Condensing long technical documents into faithful summaries, with the source always one click away for verification.

The need

A large program generates more documents than any person can read: analysis reports, run logs, review notes, meeting records. Summarization tools condense these into digestible overviews so that the right people notice the right things. The value is triage, helping a reader decide what to read in full, not replacing the full document.

What good summarization preserves

Kronos motion — long view

The faithfulness problem

Automated summarizers can introduce errors: dropping a critical caveat, blurring an uncertain result into a confident one, or stating something the source never said. In technical work these errors are dangerous because the summary reads fluently and the mistake is invisible without checking the source. So summaries are treated as pointers, always keeping the original accessible, never as replacements for it.

Keeping numbers honest

A particular risk is numerical distortion, a summary that reports a value without its conditions or uncertainty. For engineering documents, quantitative claims are better pulled verbatim with their context than paraphrased. A summary that says 'gain of about 3' where the source says 'Q 3.424 under stated modeling assumptions' has lost the very thing an engineer needs.

python
def summarize(doc, extract, condense):
    facts = extract(doc)                 # pull numbers verbatim with context
    prose = condense(doc.narrative)      # compress the discussion
    return {'summary': prose, 'facts': facts, 'source': doc.id}

Kronos framing

Summarization supports internal navigation of a large body of analysis, always alongside the reproducible source. It never becomes the record of a result; the record is the reproducible artifact behind it. Consistent with house style, summaries avoid filler and hype.