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Evidence record 4497 · automatically gathered

Measuring What Matters Beyond Text: Evaluating Multimodal Summaries by Quality, Alignment, and Diversity

Multimodal Large Language Models (MLLMs) have facilitated Multimodal Summarization with Multimodal Output (MSMO), wherein systems generate concise textual summaries accompanied by salient visuals from multimodal sources. However, current MSMO evaluation remains fragmented: text quality, image-text alignment, and visual diversity are typically assessed in isolation using unimodal metrics, making it difficult to capture whether the modalities jointly support a faithful and useful summary. To addre

Record details

Published: 12 May 2026
Source: arXiv
Category: Research
Topics: Safety & alignment
Retrieved: 14 July 2026

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How to cite this record

ethics.ai (12 May 2026), “Measuring What Matters Beyond Text: Evaluating Multimodal Summaries by Quality, Alignment, and Diversity,” evidence record 4497, https://ethics.ai/record/4497 (originally published by arXiv).

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