Long-Document MapReduce-based Summarization Algorithm

A Long-Document MapReduce-based Summarization Algorithm is a LLM-based long document summarization algorithm that employs the MapReduce programming model.

• Context:
  • It can (typically) divide the task into smaller, manageable tasks (map phase on small sub-documents) and then consolidate the results into a cohesive summary (reduce phase).
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• Example(s):
  • as proposed in (Chakraborty, 2023).
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• Counter-Example(s):
  • Stuffing Method LLM-based Long Document Summarization Algorithm that attempt to condense a large document into the context window of a single LLM request, often failing due to token limit constraints.
  • Refine Method LLM-based Long Document Summarization Algorithm that incrementally build a summary by iteratively refining the output with successive segments of the document, focusing on maintaining context continuity but may be less efficient for very large documents.
• See: MapReduce, Text Summarization, Natural Language Processing (NLP), Large Language Models (LLMs), Langchain MapReduceChain Component.

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References

2024

• "Text Summarization of Large Documents using LangChain." Github Notebook.
  • NOTES:
    • The MapReduce method for document summarization employs a multi-stage process to summarize extensive texts, ideally suited for large documents by breaking them into smaller chunks and summarizing each before combining them into a unified summary.
    • Utilizes LangChain's MapReduceDocumentsChain within the load_summarize_chain method, specifying map_reduce as the chain_type to efficiently manage and summarize large pieces of text.
    • For a 32-page document, the map_reduce chain segments the document into maximum 1024 token chunks, applying an initial prompt to each chunk to generate individual summaries.
    • The map prompt used for initial summarization of each chunk: {text}. BULLET POINT SUMMARY:
    • Following the generation of chunk summaries, a combine prompt merges these into a comprehensive document summary: ```Write a summary of the entire document that includes the main points from all of the individual summaries.```
    • Prompts are defined using PromptTemplate with specified templates for mapping and combining phases to guide the summarization process.
    • The map_reduce_chain is initialized with parameters including vertex_llm_text, chain_type, map_prompt, combine_prompt, and return_intermediate_steps set to True.
    • Summaries are generated using the map_reduce_chain, leveraging a tokenizer with a 1024 token limit by default to process input documents.
    • The results are organized and validated through a Pandas DataFrame, listing input documents alongside their corresponding summaries, facilitating easy review and analysis.
    • This method overcomes the limitations of the stuffing method by allowing for parallel processing, though it requires multiple calls to the model and may risk losing context between document sections.
    • Despite potential context loss, the MapReduce method provides a scalable solution for summarizing large documents efficiently.

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 introduces the Long-Document MapReduce-based Summarization Algorithm as an efficient approach for summarizing extensive documents by utilizing the MapReduce programming model to overcome the token limit constraints of Large Language Models (LLMs).
    • This approach segments the document into smaller parts, applies summarization individually, and then combines these summaries, demonstrating an effective solution to the challenges posed by large document summarization with LLMs.
    • By leveraging distributed computing and parallel processing, the algorithm enhances the scalability and efficiency of the summarization process, making it particularly suitable for processing documents that exceed the LLM context window or standard Natural Language Processing (NLP) model limits.
    • Highlights the potential of this algorithm to significantly reduce processing time and resource consumption in the context of large document summarization tasks.

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