This paper proposes NOrmed Index of Retention (NOIR), a quantitative objective metric for evaluating summarization quality of arbitrary texts that relies on both the retention of semantic meaning and the summary length compression. This gives a measure of how well the recall-compression tradeoff is managed, the most important skill in summarization. Experiments demonstrate that NOIR effectively captures the token-length / semantic retention tradeoff of a summarizer and correlates to human perception of sumarization quality. Using a language model-embedding to measure semantic similarity, it provides an automated alternative for assessing summarization quality without relying on time-consuming human-generated reference summaries. The proposed metric can be applied to various summarization tasks, offering an automated tool for evaluating and improving summarization algorithms, summarization prompts, and synthetically-generated summaries.