Mastering Text Summarization: Three Techniques for Handling Documents Beyond the LLM Context Window

Text summarization can be challenging when your document doesn't fit into the LLM's context window. Here are 3 common text summarization techniques:

Stuffing

This method is straightforward and efficient when working with shorter documents. If your LLM's context window is large enough, you can simply pass the entire document to the LLM via the prompt. The model then processes the full text at once, generating a comprehensive summary. This approach is ideal for documents that fit comfortably within the model's token limit.

Map Reduce

When dealing with longer documents that exceed the LLM's context window, the Map Reduce method comes in handy. This technique involves breaking down the document into smaller, manageable chunks. Here's how it works:

1. Split the document into smaller pieces (chunks).
2. Generate a summary for each chunk independently.
3. Combine all the individual summaries into a single, cohesive summary at the end.

This approach allows you to process large documents by summarizing sections separately and then synthesizing the results.

Refine

The Refine method is another effective technique for summarizing lengthy documents. Similar to Map Reduce, it starts by chunking the document into smaller pieces. However, the summarization process is more iterative:

1. Divide the document into chunks.
2. Summarize the first chunk.
3. For each subsequent chunk, summarize it while including the summary from the previous step.
4. Repeat this process until you've summarized the entire document.

This method allows for a more contextual summary, as each step builds upon the previous summaries, potentially capturing more nuanced relationships between different parts of the document.

Each of these methods has its strengths and is suited for different scenarios. Stuffing is great for shorter documents, while Map Reduce and Refine offer solutions for longer texts that exceed the LLM's context window. The choice between Map Reduce and Refine may depend on the specific requirements of your summarization task and the nature of the document being summarized.

By understanding and applying these techniques, you can effectively summarize documents of various lengths, ensuring that you capture the essential information even when working with large texts that don't fit into a single LLM context window.