Chunk large complex PDFs to summarize using LLM

Introduction

In this article, we discuss a technique to parse and summarize large PDFs while maintaining their context. The technique leverages a method known as MapReduce and is supported by a detailed code implementation.

Motivation Behind the Technique

The motivation for developing this summarization technique arises from two primary needs. Firstly, there is an overwhelming number of archived papers on large language models available online. Reading through each paper entirely can be time-consuming given the rapid evolution of language models and generative AI. A quicker way to grasp the essence of these papers is essential for staying updated in the field. The second motivation, while confidential, reinforces the need for effective knowledge extraction from complex documents.

Challenges Faced in Summarization

Summarizing large PDFs comes with its challenges. The primary ones include:

1. Unstructured Format: PDF documents often lack a standardized format, making it difficult to chunk the content while maintaining its context. Arbitrary chunking can result in losing essential context between sections.

2. Complex Tables: Many PDFs contain intricate tables that are not merely simple row-column arrangements. Previous attempts to extract tables using tools like Tabula and PyMuPDF faced issues with accuracy, leading to disarrangements that hindered understanding. A traditional machine learning approach for layout parsing may be necessary for better extraction.

3. Contextual Understanding of Table Content: After extracting table data, it's crucial to formulate the content in a way that makes it comprehensible to language models, ensuring accurate interpretation and summarization.

The Summarization Process

To implement the parsing and summarization, I employed Adobe’s Extract API, which provides structured outputs in JSON format along with external Excel documents for any extracted tables. The Adobe Extract API is a cloud-based service that utilizes Adobe Sensei’s AI capabilities to extract text, tables, and figures from PDF documents, both scanned and native.

The Extraction Phase

Once I extracted the PDF document, I received a JSON structure representing the layout of the document along with tables in Excel format. For instance, by examining the sections of an archived paper titled "Lost in the Middle: How Language Models Use Long Context," I was able to categorize content into defined sections seamlessly.

Chunking the Content

To maintain context while chunking, I wrote a custom JSON parser that segmented the document into related sections based on headings. This step ensured that each chunk contained complete and coherent sections of the original document. Each section was extracted into distinct files based on headings, allowing me to preserve the necessary context.

Summarization Using LangChain

After chunking, I turned to LangChain’s implementation of the MapReduce summarization technique. The process involves:

1. Loading the Chunks: Using a text loader, I converted the newly created chunks into a document schema used by LangChain.

2. Map Chain Creation: This step involved creating a map prompt via LangChain Hub that would summarize each section individually.

3. Reduce Chain Creation: The reduce prompt takes the summaries from the map chain and consolidates them into a final summarized version of the entire document.

Code Overview

The code is structured to first handle input and output paths for the extracted PDF, manage Adobe Extract API credentials, and then implement the chunking process followed by the summarization flow. A clear sequence of code segments allows for easy tracking of how the document is processed, summarized, and finally vacuums up meaningful insights into concise summaries.

Final Output

When the MapReduce chain was executed, it produced succinct summaries that outlined the central themes and findings of the original PDF document. This final summary encapsulated major insights and provided a clear context of the document’s content.

Conclusion

This method provides a robust framework for parsing and summarizing large, complex PDFs while retaining the necessary context for accurate understanding.

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Keywords

• Large Language Models
• PDF Summarization
• Contextual Chunking
• MapReduce Technique
• Adobe Extract API
• LangChain

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FAQ

Q1: What is the primary motivation for summarizing large PDFs?
A1: The primary motivation is to efficiently grasp the concepts of numerous archived papers in the rapidly evolving field of language models and generative AI.

Q2: What are the main challenges of summarizing large PDFs?
A2: The main challenges include the unstructured nature of PDFs, the complexity of the tables within them, and the requirement for clear content interpretation via language models.

Q3: How does the Adobe Extract API assist in this process?
A3: The Adobe Extract API extracts both text and structural data from PDFs, providing a JSON format for layout and separate Excel files for tables.

Q4: What role does LangChain play in the summarization process?
A4: LangChain facilitates the summarization process through its MapReduce implementation, which includes loading document chunks, creating mapping and reducing chains, and generating concise summaries.

Q5: Can this technique be applied to other document types?
A5: While this technique is tailored for PDFs, the underlying principles can be adapted to other document types with similar unstructured content challenges.