How to Intermediate · 3 min read

Hierarchical summarization explained

Quick answer

Hierarchical summarization is a technique where large documents are split into smaller chunks, each chunk is summarized individually using a language model, and then these summaries are recursively combined to produce a concise overall summary. This approach efficiently manages large context windows beyond a single LLM input limit by summarizing in multiple levels.

PREREQUISITES

Python 3.8+
OpenAI API key (free tier works)
pip install openai>=1.0

Setup

Install the openai Python package and set your API key as an environment variable for authentication.

bash

pip install openai

output

Collecting openai
  Downloading openai-1.x.x-py3-none-any.whl (xx kB)
Installing collected packages: openai
Successfully installed openai-1.x.x

Step by step

This example demonstrates hierarchical summarization by splitting a long text into chunks, summarizing each chunk with gpt-4o, then summarizing the combined chunk summaries to get a final summary.

python

import os
from openai import OpenAI

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

# Sample long text split into chunks
chunks = [
    "Chunk 1: The history of AI began in the 1950s...",
    "Chunk 2: In recent years, large language models have advanced...",
    "Chunk 3: Applications of AI include healthcare, finance, and more..."
]

# Function to summarize a single chunk

def summarize_chunk(text):
    response = client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": f"Summarize this text concisely:\n{text}"}]
    )
    return response.choices[0].message.content.strip()

# Summarize each chunk
chunk_summaries = [summarize_chunk(chunk) for chunk in chunks]

# Combine chunk summaries into one text
combined_summary_text = "\n".join(chunk_summaries)

# Summarize the combined summaries for final output
final_summary = summarize_chunk(combined_summary_text)

print("Final hierarchical summary:\n", final_summary)

output

Final hierarchical summary:
 AI's history started in the 1950s, evolving rapidly with large language models recently. Its applications span healthcare, finance, and more, showcasing broad impact.

Common variations

Use asynchronous calls with asyncio for parallel chunk summarization.
Adjust chunk size based on LLM context window limits.
Use different models like gpt-4o-mini for cost efficiency or claude-3-5-sonnet-20241022 for alternative performance.
Implement multi-level recursion for very large documents by summarizing summaries repeatedly.

Troubleshooting

If summaries are too generic, provide more detailed instructions in the prompt.
If you hit rate limits, add retry logic or slow down requests.
Ensure chunk sizes fit within the model's token limit to avoid truncation.
Check environment variable OPENAI_API_KEY is set correctly to avoid authentication errors.

✅

Key Takeaways

Hierarchical summarization manages large texts by chunking and recursively summarizing to fit LLM context limits.
Use concise prompts for chunk summaries to maintain clarity in the final summary.
Adjust chunk size and model choice based on cost and performance needs.
Async processing can speed up summarization of multiple chunks.
Always verify API keys and token limits to avoid errors.

Verified 2026-04 · gpt-4o, gpt-4o-mini, claude-3-5-sonnet-20241022

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