summarization
**Text Summarization with LLMs**
**Summarization Approaches**
**Extractive Summarization**
Select and combine important sentences from source text.
- Preserves original wording
- May miss key points between sentences
- More faithful to source
**Abstractive Summarization**
Generate new text that captures the meaning.
- More natural, readable output
- Can paraphrase and synthesize
- Risk of hallucination
- LLMs excel at this approach
**Summarization Techniques**
**Simple Prompting**
```python
summary = llm.generate(f"""
Summarize the following in 3 sentences:
{long_text}
""")
```
**Hierarchical Summarization**
For very long documents:
```
[Document]
|
v
[Chunk 1] [Chunk 2] [Chunk 3] [Chunk 4]
| | | |
v v v v
[Summary1] [Summary2] [Summary3] [Summary4]
|_________|_________|_________|
|
v
[Final Summary]
```
**Map-Reduce Pattern**
```python
def map_reduce_summarize(documents: list) -> str:
# Map: Summarize each document
summaries = [llm.summarize(doc) for doc in documents]
# Reduce: Combine summaries
combined = llm.generate(f"Combine these summaries: {summaries}")
return combined
```
**Prompt Techniques**
**Length Control**
```
Summarize in exactly 100 words.
Provide a 1-paragraph summary.
Create a 3-bullet summary.
```
**Focus Control**
```
Summarize, focusing on financial information.
Summarize the key technical details.
Extract the main action items.
```
**Format Control**
```
Summarize as bullet points.
Summarize in a table with columns: Topic, Key Point, Details.
Provide a TL;DR followed by detailed summary.
```
**Use Cases**
| Use Case | Approach |
|----------|----------|
| News articles | Concise abstractive |
| Research papers | Structured: abstract, methods, findings |
| Meeting notes | Action items + discussion summary |
| Code documentation | What it does, key functions |
**Quality Considerations**
- Check for hallucinated facts
- Verify key information preserved
- Consider multiple summarizations for important docs
- Use human evaluation for quality-critical applications