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New metrics proposed for abstractiveness in text summarization · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced new metrics—Reference Abstraction (RA), Summary Abstraction (SA), and Abstraction Ratio (AR)—to better evaluate the abstractiveness of text summarization models. These metrics aim to quantify how much a generated summary deviates from simply copying source text, moving beyond traditional measures like ROUGE. Empirical validation on the XSum dataset using models like BART-large-cnn and Pegasus-xsum showed that these metrics can effectively distinguish between extractive and abstractive summarization approaches, with the Abstraction Ratio also flagging potential hallucinations. AI

IMPACT These new metrics could lead to more accurate evaluations of summarization models, driving improvements in their ability to generate concise and non-hallucinatory summaries.

RANK_REASON The cluster contains a research paper detailing new metrics for evaluating text summarization models.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New metrics proposed for abstractiveness in text summarization · 2 sources tracked

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The cluster contains a research paper detailing new metrics for evaluating text summarization models.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Praveenkumar Katwe, Rakesh Chandra Balabantaray, Kali Prasad Vittala ·

    Abstractiveness Metrics for Evaluating Text Summarization: A Refined Formulation with Empirical Validation

    arXiv:2607.10806v1 Announce Type: cross Abstract: 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 …

  2. arXiv cs.CL TIER_1 English(EN) · Kali Prasad Vittala ·

    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…

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