LLM summarization isn’t just about boiling down text; it’s about identifying the essence that remains coherent and informative even when the original source is a sprawling novel.
Let’s see this in action. Imagine we have a lengthy article about the history of quantum computing, and we want to distill it into a few key takeaways.
from transformers import pipeline
# Assume 'long_document.txt' contains a very long text
with open('long_document.txt', 'r') as f:
document = f.read()
# A common summarization pipeline, but we need to be smart about length
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
# Problem: BART-CNN has a max input token limit (often 1024 tokens)
# We can't just pass the whole document.
# Solution: Chunking and Hierarchical Summarization
def chunk_text(text, chunk_size=800, overlap=100):
"""Splits text into manageable chunks, with overlap."""
tokens = text.split() # Simple split for demonstration, a proper tokenizer is better
chunks = []
start = 0
while start < len(tokens):
end = min(start + chunk_size, len(tokens))
chunks.append(" ".join(tokens[start:end]))
if end == len(tokens):
break
start += chunk_size - overlap
return chunks
def hierarchical_summarize(document, summarizer, chunk_size=800, overlap=100, max_summary_length=150, min_summary_length=50):
"""Summarizes a long document by summarizing chunks and then summarizing those summaries."""
chunks = chunk_text(document, chunk_size, overlap)
chunk_summaries = []
print(f"Processing {len(chunks)} chunks...")
for i, chunk in enumerate(chunks):
print(f"Summarizing chunk {i+1}/{len(chunks)}...")
# Ensure chunk fits model's max input length if it's not already handled by chunk_size
# For BART, this is typically 1024 tokens. Our chunk_size is already less.
summary = summarizer(chunk, max_length=max_summary_length, min_length=min_summary_length, do_sample=False)[0]['summary_text']
chunk_summaries.append(summary)
print("Concatenating chunk summaries...")
combined_summary_text = " ".join(chunk_summaries)
print("Generating final summary from chunk summaries...")
# If the combined summary is still too long, we might need another layer of summarization
# For simplicity, we'll assume it fits for now, or the summarizer handles truncation.
final_summary = summarizer(combined_summary_text, max_length=max_summary_length, min_length=min_summary_length, do_sample=False)[0]['summary_text']
return final_summary
# Let's pretend 'long_document.txt' has content.
# For demonstration, we'll create a dummy long string.
dummy_long_text = "This is the first sentence. " * 500 + "This is the second sentence. " * 500 + "This is the third sentence. " * 500
# In a real scenario, you'd load 'long_document.txt'
# Example usage:
# final_summary = hierarchical_summarize(dummy_long_text, summarizer)
# print("\n--- Final Summary ---")
# print(final_summary)
The core problem LLMs face with long documents is their fixed context window. Models like BART or T5 are typically trained on inputs up to 512 or 1024 tokens. Anything beyond that gets truncated, meaning the LLM literally cannot see the rest of the text. Trying to summarize a 10,000-token document by just feeding it to a 1024-token model means you’re only summarizing the first 1024 tokens.
The most effective strategy is hierarchical summarization. This involves breaking the long document into smaller, manageable chunks that do fit within the LLM’s context window. Each chunk is summarized independently. Then, these individual chunk summaries are concatenated and summarized again to produce a final, cohesive summary of the entire document. The overlap parameter is crucial; it ensures that context isn’t lost at the boundaries between chunks. If chunk 1 ends at token 800 and chunk 2 starts at token 801, information discussed across that boundary might be missed. Overlapping by 100 tokens means chunk 2 starts summarization from token 701, re-including some of chunk 1’s content.
The chunk_text function (using a simple split() here for clarity, but a proper tokenizer like tiktoken or transformers’ AutoTokenizer is recommended for accuracy) divides the text. The hierarchical_summarize function orchestrates the process: it calls chunk_text, iterates through each chunk, generates a summary for it using the summarizer pipeline, collects these intermediate summaries, and finally feeds the combined intermediate summaries into the summarizer one last time. The max_length and min_length parameters for the summarizer are important for controlling the output length at each stage.
A key consideration is the choice of summarization model. Models like facebook/bart-large-cnn are trained on news articles and are good general-purpose summarizers. For very technical or domain-specific documents, fine-tuning a model on similar data or using a model specifically trained for that domain (e.g., allenai/led-large-16384-arxiv for scientific papers, which has a larger context window) can yield better results. The LED (Longformer-Encoder-Decoder) architecture is specifically designed to handle much longer sequences, sometimes up to 16,000 tokens, which can reduce the need for aggressive chunking.
The do_sample=False argument in the summarizer call ensures deterministic output for a given input and model, which is usually preferred for summarization tasks where you want consistent results. If you were experimenting or wanted more varied summaries, do_sample=True with top_p or temperature sampling could be used.
The most surprising thing about handling long documents is how much information can be lost even with a good chunking strategy. The summaries of summaries are, by definition, abstractions of abstractions. The nuance and specific details present in the original document, or even in the first-level chunk summaries, can be diluted or entirely omitted in the final output. This is why iterative refinement, or even allowing the user to guide the summarization (e.g., "focus on the economic impact"), becomes important for critical applications. The model is essentially making a series of "best guesses" about what’s important at each stage, and these guesses compound.
The next challenge you’ll face is dealing with documents where the order of information is critical, or where very specific, low-frequency entities (like obscure historical figures or specific technical terms) are important.