LLM-based Long Document Summarization System

An LLM-based Long Document Summarization System is a long document summarization system that is an LLM-based system and implements an LLM-based long document summarization algorithm.

• Context:
  • It can (typically) handle documents that ... LLM context window.
  • It be a LangChain strategies, such as the Stuffing Method, MapReduce Method, and Refine Method, to enhance the summarization process, as detailed by Anirban Chakraborty in his exploration of LangChain approaches for large document summarization using Google Cloud Vertex AI PaLM2 API.
  • ...
• Example(s):
  • LLM-based Long Contract Summarization System.
  • ...
• Counter-Example(s):
  • Extractive Long Document Summarization System.
  • Manual Long Document Summarization Process that rely on human interpretation without the support of AI technologies.
• See: Large Language Models (LLMs), Abstractive Summarization, Natural Language Processing (NLP), LangChain.

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References

2023

• (Chakraborty, 2023) ⇒ Anirban Chakraborty. (2023). “Challenges of LLM for Large Document Summarization: Exploring different LangChain approaches using Google Cloud Vertex AI PaLM2 API." In: Google Cloud - Community.
  • NOTES:
    • It highlights the complexities and challenges involved in summarizing large documents using LLMs.
    • Describes the use of Google Cloud's Vertex AI PaLM2 API as an optimized tool for natural language tasks, including summarization.
    • Introduces LangChain as a framework to enhance LLM applications for document summarization, specifically designed to tackle the limitations of LLMs in handling extensive texts.
    • Discusses various LangChain strategies like Stuffing Method, MapReduce Method, and Refine Method, each with unique approaches to manage the summarization of large documents.
    • Points out the limitations of the Stuffing Method due to LLMs' context length constraints, making it less suitable for very large documents.
    • Elaborates on the MapReduce Method as a means to process documents in sections, allowing for parallel processing and overcoming size limitations.
    • Highlights the Refine Method for its ability to maintain context and continuity across different sections of a document by sequentially processing and refining the summary.

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