Abstractiveness Metrics for Evaluating Text Summarization: A Refined Formulation with Empirical Validation
Quantifying abstractiveness in generated summaries is essential for evaluating summarization models beyond surface-level metrics like ROUGE. We introduce Reference Abstraction (RA), Summary Abstraction (SA), and Abstraction Ratio (AR) -- a set of principled heuristic metrics that measure how much a summary diverges from extractive copying of the source text. The formulation uses the harmonic mean of document lengths modulated by a cubic non-overlap factor, yielding dimensionally consistent, bounded output with non-linear sensitivity to the extractive-abstractive boundary. Evaluation on 100 XSUM documents across four summarization models (BART-large-cnn, Pegasus-xsum, DistilBart, MT5-small...
| Authors | Praveenkumar Katwe, Rakesh Chandra Balabantaray, Kali Prasad Vittala |
|---|---|
| Primary category | cs.CL |
| Published | 12 July 2026 |
| Last updated at source | 12 July 2026 |
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