Summarization

Summarization is the use of an LLM to condense a longer source text into a shorter version that preserves its essential information, key points, and (ideally) intent. There are two main technical approaches: extractive summarization, which selects and stitches together the most important existing sentences from the source (older, pre-LLM technique, still used for speed-sensitive or low-hallucination-risk applications), and abstractive summarization, which generates entirely new sentences that paraphrase and compress the source's meaning — the dominant approach with modern LLMs, since they can capture nuance and produce more natural, readable output, at the cost of a small hallucination risk (the model stating something plausible-sounding but not actually present in the source). Why it matters for SaaS builders: summarization is one of the highest-ROI, lowest-friction AI features to bolt onto an existing product — meeting-notes tools summarizing transcripts into action items, email clients offering "TL;DR" previews of long threads, legal-tech tools condensing contracts into key-clause digests, research tools summarizing papers, and customer-support tools summarizing long ticket histories for agent handoff. It's cheap to implement (a single well-crafted prompt against an existing LLM API call) and has an immediately obvious, demonstrable value to end users. A concrete worked example — a customer-support SaaS auto-summarizing tickets for handoff: (1) when a ticket is reassigned to a new agent, the app pulls the full conversation history, which could easily be 50+ messages across weeks of back-and-forth; (2) it sends a prompt: "Summarize this support conversation in under 100 words for a new agent taking over. Include: the customer's core issue, what's already been tried, and the current status. Conversation: {history}"; (3) the LLM returns a tight summary that appears pinned at the top of the ticket view, above the full raw message thread; (4) the new agent reads the summary in a few seconds instead of scrolling through and reconstructing context from 50 messages, and can expand the full thread only if they need to verify a specific detail. For very long documents that exceed the model's context window (a full year of support history, a lengthy legal contract, a book-length transcript), builders chunk the source into overlapping segments and use a map-reduce summarization pattern: summarize each chunk independently, then feed those chunk-level summaries back into the model as input for a final pass that summarizes the summaries into one coherent whole — this avoids the accuracy loss that comes from naively truncating a document that's too long to fit in a single request.