Yes, large language models (LLMs) can analyze and summarize large documents, but their effectiveness depends on how they’re implemented and the constraints of the model. LLMs process text by breaking it into tokens—units of text like words or subwords. Most models have a maximum token limit for input and output (e.g., 4,000–128,000 tokens depending on the model). For documents exceeding this limit, developers must split the text into chunks, analyze each section, and combine results. For example, a 100-page technical report might be divided into chapters, summarized individually, and then synthesized into a final summary. Tools like LangChain or LlamaIndex provide frameworks to manage this process, handling chunking, context retention, and aggregation.
LLMs perform summarization using two primary methods: extractive (selecting key sentences) and abstractive (generating new sentences). For technical documents like API specifications, an LLM might extract critical endpoints and parameters while rewriting explanations in simpler terms. However, accuracy depends on the model’s training data and the clarity of the source material. For instance, summarizing a legal contract requires the model to identify clauses, obligations, and deadlines—tasks that can be error-prone if the language is ambiguous. Developers can improve results by fine-tuning models on domain-specific data or using retrieval-augmented generation (RAG) to pull relevant context from external databases. Preprocessing steps like removing redundant text or structuring the input with headers also help the model focus on essential content.
Challenges arise when working with very large or complex documents. Context loss between chunks, inconsistent terminology, and overlapping themes can degrade summary quality. For example, a research paper with interconnected sections might lose nuance if summarized in isolation. To mitigate this, developers can implement hierarchical summarization: create section summaries first, then combine them into a high-level overview. Post-processing steps like validation against the source text or human review are often necessary. Additionally, newer models like GPT-4 Turbo with extended context windows (up to 128k tokens) reduce the need for chunking, but costs and latency increase. Balancing these trade-offs requires testing different chunk sizes, model configurations, and validation methods to ensure summaries are both concise and accurate.