Technical

Summarization is not just an LLM wrapper

Mar 22, 2024

Aneesh Arora

Cofounder and CTO

Good summarization requires far more work than just prompting an LLM

Summarization is not just simply asking a Large Language Model (LLM) to summarize a piece of text with a prompt. Of course, you can go about it that way, but you will get poor results.

Key Steps for Effective LLM Summarization:

1. Information Extraction: The initial step in summarization involves extracting information. Afterword is capable of processing videos, audio, and text in multiple formats, including web articles, PDFs, DOCX files, TXT files, and e-books (EPUB). Each format presents its unique challenges, with some being more complex than others. Particularly, web articles pose a considerable challenge due to the lack of standardization in HTML usage.
2. Semantic Chunking: After acquiring the text for summarization, it often exceeds the context window limits of most Large Language Models (LLMs). Although LLMs are gradually expanding their context windows, it remains impractical to include the entire text in a single prompt. This is because LLMs might focus more on the beginning and end of overly large prompts, neglecting the middle sections. The more you rely on them without guidelines, the less control you have over the resulting summaries.
   
   Therefore, the proper method for creating summaries involves semantically chunking the text; that is, identifying subtopics within the larger text and grouping sections accordingly while preserving the original structure. After semantic chunking is complete and each individual chunk fits within the context window of the LLM being used, we can then begin the process of prompt engineering.
3. Prompt Engineering: Prompt engineering resembles a form of 21st-century alchemy or akin to communicating with an extraterrestrial being. It requires deciphering the precise instruction that will yield the desired outcome. Moreover, it's crucial to ensure that your prompt consistently delivers this result with each invocation of the LLM. Additionally, you must instruct the prompt to structure its output in a manner that allows traditional deterministic computer code to parse it and extract the necessary information. Nowadays, we have LLMs capable of producing output in JSON format, should you prefer it.
4. User Experience: Now, most of your task is completed, but a crucial aspect remains: enhancing the user experience. LLMs can be slow to synthesize and produce output, so how do you maintain user engagement during this time? The solution is to provide updates throughout the processing phase to reassure users that progress is being made. Additionally, deliver the summary progressively as each chunk is processed. I've discussed this topic in great detail in an earlier blog post, so you can go read that .