In the rapidly evolving world of AI, long-form summarization has become a critical task. But how do we truly know if these summaries are capturing all the essential information? Traditional metrics like ROUGE and BERT often fall short, focusing on surface similarities rather than true understanding. A new research paper introduces EVA-Score, a novel approach to evaluating the informativeness of abstractive summaries. Imagine a detective meticulously piecing together clues. EVA-Score works similarly, extracting "atomic facts" from both the original text and the generated summary. It then links these facts into logical chains, like constructing a narrative thread, to ensure contextual integrity. The innovation doesn't stop there. Recognizing that relationships between ideas can span beyond individual sentences, EVA-Score employs document-level relation extraction, adding another layer of depth to its analysis. Finally, it uses LLMs like a discerning fact-checker to validate the extracted information against the original document. This multi-faceted approach results in a score that reflects the summary's true informativeness. The researchers tested EVA-Score against traditional methods and found it had significantly higher correlation with human evaluations, particularly when discerning nuanced differences between summaries. This suggests EVA-Score is a promising step towards building AI that not only summarizes but truly *understands*. While computationally intensive, EVA-Score offers explainable, quantitative results that can help guide future development of more informative summarization models. The focus on informativeness opens doors to applications beyond summarization, paving the way for AI systems capable of more sophisticated knowledge extraction and reasoning.