An Automated Length-Aware Quality Metric for Summarization

• URL: http://arxiv.org/abs/2507.07653v1
• Date: Thu, 10 Jul 2025 11:25:16 GMT
• Title: An Automated Length-Aware Quality Metric for Summarization
• Authors: Andrew D. Foland,
• Abstract summary: This paper proposes NOrmed Index of Retention (NOIR), a quantitative objective metric for evaluating summarization quality of arbitrary texts.<n>Experiments demonstrate NOIR effectively captures the token-length / semantic retention of a summarizer and correlates to human perception of sumarization quality.<n>The proposed metric can be applied to various summarization tasks, offering an automated tool for evaluating and improving summarization algorithms.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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