The best fit is Implement an LLM-based document summarization assistant. This approach directly meets the requirement to condense long legal documents and surface the main points so attorneys can work faster.

Implement an LLM-based document summarization assistant can produce concise extractive or abstractive summaries and highlight action items and key facts. It works with long documents by chunking text and using retrieval augmented generation with embeddings so the assistant can reference the full corpus without exceeding model context. This approach scales to tens of thousands of pages per month and supports quality controls such as human review and iterative prompting to ensure legal accuracy.

Build a multilingual translation workflow focuses on converting text between languages and does not reduce or condense content or extract the most relevant points for legal review.

Create a recommendation engine for related cases is intended to rank or suggest related items and it does not extract or summarize the contents of a single long document.

Amazon Comprehend can identify entities and key phrases but it does not produce comprehensive, coherent summaries of lengthy filings and it is not optimized for abstractive summarization the way an LLM assistant is.

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