Summarization | HyperKit.ai

Definition

Summarization is the task of condensing longer text into shorter versions while preserving key information and meaning. LLMs excel at both extractive (selecting key sentences) and abstractive (generating new summary text) summarization.

Effective summarization is crucial for managing context windows, creating digests, and helping users quickly understand large documents.

Key Concepts

Extractive: Selecting and combining existing sentences
Abstractive: Generating new text that captures meaning
Compression ratio: Original length vs summary length
Faithfulness: Summary accuracy to source material

Examples

Types

Extractive vs Abstractive Summarization

ORIGINAL TEXT:
"The company announced quarterly revenue of $5.2
billion, exceeding analyst expectations of $4.8
billion. The CEO attributed the strong performance
to increased cloud services adoption, which grew
45% year-over-year. However, hardware sales declined
12% due to supply chain challenges. The company
plans to invest $2 billion in AI research over the
next two years. Stock price rose 8% after hours."

EXTRACTIVE SUMMARY:
(Selects key sentences verbatim)
"The company announced quarterly revenue of $5.2
billion, exceeding analyst expectations. Cloud
services grew 45% year-over-year. Stock price
rose 8% after hours."

ABSTRACTIVE SUMMARY:
(Generates new condensed text)
"Revenue beat expectations at $5.2B, driven by 45%
cloud growth despite 12% hardware decline. The
company will invest $2B in AI, boosting stock 8%."

COMPARISON:
┌─────────────────┬────────────┬─────────────┐
│ Aspect          │ Extractive │ Abstractive │
├─────────────────┼────────────┼─────────────┤
│ Faithfulness    │ High       │ Medium*     │
│ Fluency         │ Medium     │ High        │
│ Compression     │ Lower      │ Higher      │
│ Hallucination   │ None       │ Possible    │
│ Coherence       │ Variable   │ Good        │
└─────────────────┴────────────┴─────────────┘
* Abstractive can introduce errors when rephrasing

Prompts

Summarization Prompt Patterns

SUMMARIZATION PROMPT PATTERNS:

1. BASIC SUMMARY:
"Summarize the following text in 2-3 sentences:
{text}"

2. LENGTH-CONTROLLED:
"Summarize in exactly 50 words:
{text}"

3. BULLET POINT SUMMARY:
"Summarize the key points as a bulleted list:
{text}"

4. AUDIENCE-SPECIFIC:
"Summarize for a non-technical executive:
{technical_text}"

5. FOCUS-DIRECTED:
"Summarize, focusing specifically on financial
implications:
{text}"

6. HIERARCHICAL SUMMARY:
"Provide:
- One-sentence summary
- Three key points
- Detailed 100-word summary

{text}"

7. FAITHFUL SUMMARY (reduces hallucination):
"Summarize the following text. Only include
information explicitly stated in the text.
Do not add inferences or external knowledge.
{text}"

CONTEXT WINDOW MANAGEMENT:
For very long documents, use recursive summarization:

Document (50k tokens)
    ↓ Split into chunks
[Chunk 1] [Chunk 2] [Chunk 3] [Chunk 4]
    ↓ Summarize each
[Sum 1]   [Sum 2]   [Sum 3]   [Sum 4]
    ↓ Combine summaries
[Combined partial summaries]
    ↓ Final summarization
[Final summary]

Interactive Exercise

✎

Write a Summary Prompt

You need to summarize a 10-page research paper for a busy executive who only has 1 minute to read. Write the prompt you would use.

Consider: length constraints, audience, focus areas, format.

Pro Tips

Specify length explicitly (words, sentences, or bullets)
Direct focus to what matters for the use case
For high-stakes summaries, ask for only explicit information
Use Chain of Density for information-rich summaries

Definition

Key Concepts

Examples

Interactive Exercise

Related Terms