What is summarization?
Summarization is the use of a language model to condense a long text into a shorter one that keeps the important points. Extractive summarization selects and stitches together sentences from the source. Abstractive summarization, which large language models do, writes new sentences in its own words. Modern systems are almost always abstractive, and the summary can be shaped by a prompt: a two-line executive summary, a bulleted list of action items, or a structured record with named fields.
The source can be anything that fits in the model’s context window: a meeting transcript, a contract, a customer’s support history, a batch of inspection reports, or a regulatory filing. Longer sources are chunked, summarized in pieces, and then summarized again.
Why summarization matters
Reading is a large hidden cost in most knowledge work. A claims adjuster reads the file before deciding. A sales representative reads the account history before calling. A compliance officer reads the new rule before advising. Summarization does not remove the need to read the source when the decision is high stakes, but it tells the reader what is in the document and where to look. For routine material it replaces the reading altogether.
Summarization in practice
A commercial lender summarizes each borrower’s quarterly financial package, including covenant compliance certificates and management commentary, into a one-page memo for the credit committee. The prompt specifies the fields: revenue trend, covenant status, management’s explanation of variances, and open questions. The analyst reads the memo, checks the flagged items against the source, and adds judgment. The committee reads a consistent one-page format for every borrower rather than a different analyst’s style each time.
What to watch for
- Check for omissions as well as errors; a summary that leaves out the one covenant breach is wrong even if every sentence in it is true.
- Ask the model to cite or quote the source passage for each key point, which makes verification fast.
- Fix the output format in the prompt so summaries are comparable across documents and over time.
- Be cautious with numbers, dates, and names, which models misstate more often than general themes; hallucination is the main risk.
- Decide where the source documents are processed, since summarization sends entire confidential files through the model.