Strategies for Summarizing and Evaluating Long PDF Documents(Development of Large Model Applications 12)

Explore breakthroughs in long document summarization and evaluation with large language models (LLMs). Learn how to create and assess high-quality summaries effectively.

Hello everyone, welcome to the "Development of Large Model Applications" column.

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Today, we explore breakthroughs in summarizing and evaluating long documents with large language models (LLMs), an essential application of LLMs.

We'll discuss using LLMs to create high-quality summaries and evaluate them comprehensively.

Document Summarization Before Modern LLMs

Document summarization is a classic topic.

Traditional methods often rely on statistics and information retrieval, like keyword extraction and sentence ranking. These methods are efficient but struggle with long documents and complex semantics.

Older LLM summarization uses two approaches: Extractive and Abstractive.

• Extractive: Select key sentences from the original text. For example, BERT-based summarization uses this method.

• Abstractive: Generates new summary text based on understanding the original. Examples include early GPT, T5, and BART-based summarization.

Let's use the classic T5 model to summarize a paper, such as the Tiny Llama paper, and see what it says.