AWS Certified Cloud Practitioner

Explanation

The requirement is to read legal documents and extract key points, which aligns perfectly with summarization tasks. Let's analyze each option:

A. Build an automatic named entity recognition system.

• Named Entity Recognition (NER) identifies specific entities like names, dates, organizations, and locations
• While useful for extracting structured information, it doesn't synthesize or extract the main points or key takeaways from documents
• NER would identify what is mentioned, but not what matters in the document

B. Create a recommendation engine.

• Recommendation engines suggest items based on user behavior, preferences, or similarity
• This is completely unrelated to extracting key points from legal documents
• Used for e-commerce, content suggestions, not document analysis

C. Develop a summarization chatbot.

• CORRECT ANSWER: A summarization chatbot built with LLMs can:
  • Read and comprehend entire legal documents
  • Identify important information and main arguments
  • Generate concise summaries highlighting key points
  • Provide interactive capabilities for users to ask follow-up questions
• LLMs are particularly well-suited for summarization tasks as they can understand context and extract meaningful insights

D. Develop a multi-language translation system.

• Translation systems convert text from one language to another
• While potentially useful for multilingual documents, this doesn't address the core requirement of extracting key points
• Translation is about language conversion, not content analysis

Why summarization is the best approach:

1. Legal documents are often lengthy and complex
2. LLMs can process and understand the context of legal terminology
3. Summarization extracts the essence without losing important details
4. A chatbot interface allows for interactive refinement of summaries

Note: While Named Entity Recognition (option A) could be a component of a larger solution, it alone doesn't meet the requirement of extracting key points. The question specifically asks for extracting key points, which is fundamentally a summarization task.