An Automated Length-Aware Quality Metric for Summarization
Fuente:
arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911049303719936 |
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| author | Foland, Andrew D. |
| author_facet | Foland, Andrew D. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_07653 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An Automated Length-Aware Quality Metric for Summarization Foland, Andrew D. Computation and Language 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. |
| title | An Automated Length-Aware Quality Metric for Summarization |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2507.07653 |