5 Levels Of LLM Summarizing: Novice to Expert
Introduction
Summarization plays a crucial role when working with language models, allowing you to distill essential information from lengthy documents such as articles, financial reports, chat history, books,
In this tutorial we will explore the five levels of summarization. By following these levels, we will gain the skills to summarize a couple of sentences, paragraphs, pages, and even entire books. We'll also uncover the techniques for summarizing an unknown amount of text using advanced methods like utilizing agents.
Level One :Basic Prompt
In the initial level, we learn the basics of summarization by using a simple prompt to summarize a couple of sentences. By importing the OpenAI API key and creating a language model,
We can generate concise summaries from provided text. We discover that adjusting the instructions to cater to a five-year-old's understanding produces more digestible summaries, showcasing the adaptability of the summarization process.
Level Two: Promote Templates-Summarize a Cupule Paragraph
On to the second level, we delve into summarizing paragraphs using prompt templates. By utilizing OpenAI's prompt templates, we can easily swap out different parts of the prompt to generate summaries.
We demonstrate this technique by summarizing two essays by Paul Graham, showcasing the flexibility and effectiveness of prompt templates in summarization tasks.
Level Three: Map Reduce-Summarize a Cupule Pages Multiple Pages
On the third level, we tackle the challenge of summarizing pages. With longer documents, handling a larger number of tokens becomes crucial. We introduce the "summarize chain" function provided by LaneChain, which enables us to perform a map-reduce operation over multiple documents.
By splitting the text into chunks and summarizing each chunk individually, we obtain a collection of summaries. Furthermore, we explore the option of obtaining summaries of the summaries, providing a condensed overview of the entire document collection.
Level Four: Best Representation Vector - Summarize an Entire Book
In the fourth level, we confront the task of summarizing an entire book. However, the sheer size of books poses a challenge for traditional summarization techniques. To overcome this, we employ an approach involving embeddings and clustering. By loading the book and splitting it into chunks, we generate vectors that represent each chunk. Employing clustering techniques like k-means, we identify similar chunks and select the most representative ones for summarization. This method allows us to summarize key sections of the book without analyzing the entire text.
Level Five: Agents- Summarize an Unknown Amount Of Text
In the fifth level, we explore the frontier of summarization—summarizing an unknown amount of text using agents.
We acknowledge that using agents for summarization is an ongoing area of research, but we provide a glimpse into its potential. With the help of a Wikipedia API wrapper, we demonstrate how agents can retrieve information from Wikipedia about specific topics. We showcase the agent's ability to gather details about Napoleon and Serena Williams, highlighting their commonalities and demonstrating the evolving nature of summarization techniques.
