As AI-generated summaries become increasingly integral to information dissemination, establishing their quality with precision is critical. This presentation goes beyond traditional validation metrics like ROUGE and BLEU, exploring more sophisticated techniques for a holistic evaluation of summary quality on Finance. We will explore industry-standard methods including semantic similarity measures and their limitations, introducing alternative approaches such as information density analysis, and question-answering frameworks for comprehensive validation. Drawing on empirical research and case studies, we aim to present a nuanced perspective on LLM summary evaluation, highlighting innovative methodologies that offer deeper insights into the summarization capabilities of AI models.