LLM Applications
LLM for Summarization â Condensing Information Intelligently
Summarization is one of the most valuable applications of LLMs, enabling automatic condensation of long documents into concise, coherent summaries. This guide covers the theoretical foundations, evaluation methodologies, and practical techniques for building effective summarization systems.
- Abstractive vs Extractive â Two fundamental summarization paradigms
- Evaluation Metrics â ROUGE, BERTScore, and human evaluation
- Long-Document Summarization â Handling documents that exceed model context windows
The art of summarization is knowing what to leave out.
LLM for Summarization
Summarization aims to produce a concise version of a longer text while preserving key information and overall meaning. LLMs have achieved state-of-the-art performance on summarization tasks through large-scale pretraining and instruction tuning.
Summarization Paradigms
Extractive Summarization
Extractive approaches have the advantage of preserving original phrasing and factual accuracy, but may produce less coherent summaries.
Abstractive Summarization
Abstractive approaches can produce more natural and coherent summaries but may introduce factual errors or hallucinations.
Hybrid Approaches
Mathematical Formulation
The model learns to generate a summary y that maximizes the conditional probability given the source document x.
Evaluation Metrics
ROUGE Scores
ROUGE-1 measures unigram overlap (individual words), ROUGE-2 measures bigram overlap (word pairs), and ROUGE-L measures longest common subsequence.
BERTScore
Evaluation Comparison
| Metric | Measures | Strengths | Weaknesses |
|---|---|---|---|
| ROUGE-1 | Word overlap | Fast, interpretable | Misses semantics |
| ROUGE-2 | Phrase overlap | Captures fluency | Limited context |
| ROUGE-L | Sentence structure | Flexible matching | Limited semantics |
| BERTScore | Semantic similarity | Captures meaning | Computationally expensive |
| Human Evaluation | Overall quality | Gold standard | Expensive, subjective |
Long-Document Summarization
Many real-world documents exceed the context window of LLMs (typically 4K-128K tokens). Several strategies address this challenge.
Chunking Strategies
- Fixed-size chunking: Split document into equal-sized segments
- Semantic chunking: Split at paragraph or section boundaries
- Sliding window: Overlapping chunks to capture context
- Hierarchical summarization: Summarize sections, then summarize section summaries
Hierarchical Summarization
The hierarchical approach first summarizes each chunk independently, then aggregates the chunk summaries into a final summary.
Map-Reduce Approach
Practical Implementation
Basic Summarization with LLMs
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "meta-llama/Llama-3-8B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
document = """Your long document text here..."""
prompt = f"""Please provide a concise summary of the following document:
{document}
Summary:"""
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
**inputs,
max_new_tokens=300,
temperature=0.3,
do_sample=True
)
summary = tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True)
print(summary)
Long-Document Summarization with Chunking
def chunk_document(text, chunk_size=2000, overlap=200):
words = text.split()
chunks = []
for i in range(0, len(words), chunk_size - overlap):
chunk = " ".join(words[i:i + chunk_size])
chunks.append(chunk)
return chunks
def hierarchical_summarize(document, model, tokenizer):
chunks = chunk_document(document, chunk_size=2000, overlap=200)
chunk_summaries = []
for chunk in chunks:
prompt = f"Summarize: {chunk}\nSummary:"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=150)
summary = tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True)
chunk_summaries.append(summary)
combined = " ".join(chunk_summaries)
final_prompt = f"Combine these summaries into a coherent summary:\n{combined}\nFinal Summary:"
inputs = tokenizer(final_prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=300)
return tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True)
Summarization Challenges
Hallucination
Mitigation strategies:
- Factual verification: Cross-reference generated claims with source
- Constrained decoding: Limit generation to phrases from the source
- Citation generation: Require citations for generated claims
Coherence and Flow
Ensuring that the summary reads naturally and maintains logical flow is challenging, especially for long documents.
Faithfulness
Best Practices
Prompt Engineering for Summarization
- Specify length: "Summarize in 3-5 sentences"
- Define focus: "Focus on the methodology and key findings"
- Set style: "Write a formal, objective summary"
- Provide examples: Include example summaries for style reference
Quality Control
- Multiple passes: Generate multiple summaries and select the best
- Fact-checking: Verify key claims against source document
- Readability testing: Ensure the summary is clear and concise
- User testing: Gather feedback from target audience
What to Learn Next
-> LLM for Question Answering Open-domain, extractive, and conversational QA with large language models.
-> LLM for Information Extraction Named entity extraction, relation extraction, and structured output generation.
-> LLM for Sentiment Analysis Aspect-based sentiment, emotion detection, and opinion mining.
-> LLM for Recommendation Systems Conversational recommenders, preference learning, and cold start solutions.
-> LLM for Content Creation Creative writing, marketing copy, and content generation at scale.
-> LLM Compliance and Governance Regulatory compliance, audit trails, and data governance for LLMs.