AI summarization produces a shorter representation of source material while preserving information judged relevant to the requested task. In business workflows it can condense supplier responses, contracts, invoices, meetings or account histories. A summary is a derived aid, not a substitute for the source record.
What kinds of summaries are used?
- Extractive summaries that select source passages
- Abstractive summaries that restate information
- Structured summaries organized into defined fields
- Comparative summaries across several documents
- Exception summaries focused on risks or differences
How should the task be specified?
Define the intended reader, decision, source set, required fields, length and treatment of uncertainty. Tell the system whether it may infer relationships or must restrict itself to explicit source statements.
What can go wrong?
A summary can omit a qualification, merge incompatible statements or present an inference as fact. Long source sets can also hide version conflicts. Material amounts, dates, obligations and exceptions should be linked back to their exact source locations.
How should summaries be controlled?
Preserve the source documents, identify the generated summary, record the instruction and model version where material, and require human review for decisions with financial, legal or operational consequences.
