LLM-based Text Summarization Algorithm

An LLM-based Text Summarization Algorithm is a neural text summarization algorithm that uses a pre-trained LLM.

• Context:
  • It can be implemented by an LLM-based Summarization System (to solve an LLM-based summarization task).
  • ...
• See: Text Summarization Algorithm.

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References

2023

• (Adams, Fabbri et al., 2023) ⇒ Griffin Adams, Alexander Fabbri, Faisal Ladhak, Eric Lehman, and Noémie Elhadad. (2023). “From Sparse to Dense: GPT-4 Summarization with Chain of Density Prompting.” doi:10.48550/arXiv.2309.04269
  • SUMMARY:
    • It introduces the Chain of Density (CoD) prompting technique to generate dense GPT-4 summaries without extending their length.
    • It employs an iterative method where GPT-4 starts with an entity-sparse summary and then incorporates missing salient entities, maintaining the summary's original length.
    • It emphasizes that CoD summaries are more abstractive, show more fusion, and reduce lead bias compared to the summaries produced by a vanilla GPT-4 prompt.
    • High-level Algorithm:
      1. Generate Initial Summary: Prompt GPT-4 to produce a verbose, sparse initial summary with minimal entities.
      2. Identify Missing Entities: Extract 1-3 concise, relevant, novel entities from the source text not in previous summary.
      3. Fuse Entities: Prompt GPT-4 to rewrite previous summary fusing in missing entities without increasing length. Employ compression and abstraction techniques to make space.
      4. Iterate: Repeat Identify Missing Entities and Fuse Entities steps multiple times, incrementally densifying summary by packing in more entities per token through rewriting.
      5. Output Chain: The final output is a chain of fixed-length summaries with increasing density produced through iterative abstraction, fusion, and compression.

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