LLM-based Long Document Summarization Algorithm

An LLM-based Long Document Summarization Algorithm is a long document summarization algorithm that is an LLM-based algorithm.

• Context:
  • It can (typically) be designed to handle the complexity and size of extensive documents that surpass the typical LLM context window limitations.
  • It can (typically) handle documents that exceed standard processing capacities, including overcoming the LLM context window limitations through innovative approaches.
  • It may incorporate LangChain Strategies, such as the Long-Document Stuffing-based Summarization Algorithm, Long-Document MapReduce-based Summarization Algorithm, and Long-Document Refinement-based Summarization Algorithm.
  • ...
• Example(s):
  • LLM-based Long Contract Summarization Algorithm (long contract summarization algorithm).
  • ...
• Counter-Example(s):
  • Extractive Long Document Summarization Systems, which rely on extracting key sentences or paragraphs without generating new text.
  • Manual Long Document Summarization Processes that depend on human interpretation and synthesis without the aid 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.

2022b

• (Ghadimi & Beigy, 2022) ⇒ Alireza Ghadimi, and Hamid Beigy. (2022). “Hybrid Multi-document Summarization Using Pre-trained Language Models.” Expert Systems with Applications 192
  • ABSTRACT: Abstractive multi-document summarization is a type of automatic text summarization. It obtains information from multiple documents and generates a human-like summary from them. In this paper, we propose an abstractive multi-document summarization method called HMSumm. The proposed method is a combination of extractive and abstractive summarization approaches. First, it constructs an extractive summary from multiple input documents, and then uses it to generate the abstractive summary. Redundant information, which is a global problem in multi-document summarization, is managed in the first step. Specifically, the determinantal point process (DPP) is used to deal with redundancy. This step also controls the length of the input sequence for the abstractive summarization process. This step has two effects: The first is to reduce the computational time, and the second is to preserve the important parts of the input documents for an abstractive summarizer. We employ a deep submodular network (DSN) to determine the quality of the sentences in the extractive summary, and use BERT-based similarityies to compute the redundancy. The obtained extractive summary is fed into BART and T5 pre-trained models to generate two abstractive summaries. We use the diversity of sentences in each summary to select one of them as the final abstractive summary. To evaluate the performance of HMSumm, we use both human evaluations and ROUGE-based assessments, and compare it with several state-of-the-art methods. We use DUC 2002, DUC 2004, Multi-News, and CNN/DailyMail datasets to evaluate the algorithms. The experimental results show that HMSumm outperforms the related state-of-the-art algorithms.

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