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Mastering Long Document Insights: Advanced Summarization with Amazon Bedrock and Anthropic Claude 2 Foundation Model
Unleash the Power of Generative AI for Comprehensive Document Analysis and Summarization
Text summarization is a standard Natural Language Processing (NLP) task that creates a concise summary of a longer text. According to LangChain, with foundation models, if the text is too long to stuff into a single prompt, we could use techniques like Map Reduce to summarize each document in a ‘map’ step and then ‘reduce’ the summaries into a final summary. This technique is commonly referred to as a ‘summary of summaries.’ In this post, we will move beyond simple summarization and explore advanced techniques to analyze long texts using Amazon Bedrock and the Anthropic Claude 2 Foundation Model.
In the spirit of season 🎃, we will analyze Bram Stoker’s original 1897 Victorian-era graphic novel, Dracula, freely available through Project Gutenberg. Written in the epistolary style, the novel is based on diary entries, telegrams, letters, and memos, providing an additional level of complexity when analyzing the longer text. We will explore elements such as the novel’s context, setting, characters, plot, literary devices, and themes aided by Generative AI.
Source Code
The source code used in this post’s demonstration is open-sourced and available on GitHub. I suggest starting with the project’s Jupyter Notebook, which contains all the examples herein plus additional examples not included in the post.
Amazon Bedrock and Anthropic Claude
AWS announced the general availability of Amazon Bedrock in late September 2023. Amazon Bedrock is a fully managed serverless service that makes foundation models (FMs) from leading AI companies, such as Cohere, Anthropic, Stability AI, Meta, and AI21 Labs, available through an application programming interface (API). The Anthropic Claude 2…
