Papers by Abid Ali
MULSUM: A Multimodal Summarization System with Vis-Aligner and Diversity-Aware Image Selection (2026.eacl-long)
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| Challenge: | Existing systems that condense text and images into concise, faithful digests are inefficient and require large fusion transformers. |
| Approach: | They propose a framework that uses image embeddings to generate a visually informed text summary and a Diversity-Aware Image Selector to maximize images-relevance to the summary. |
| Outcome: | The proposed framework outperforms baselines on automatic metrics such as ROUGE and human evaluation shows that selected images act as explanatory evidence rather than ornamental add-ons. |
Towards Visually Grounded Multimodal Summarization via Cross-Modal Transformer and Gated Attention (2026.findings-acl)
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| Challenge: | Existing methods for multimodal summarization often inject shallow visual features into deep models, leading to representational mismatches and weak cross-modal grounding. |
| Approach: | They propose a framework that performs text summarization and representative image selection . a deep visual processor aligns the visual encoder with the language model at corresponding depths . |
| Outcome: | The proposed framework produces more accurate, visually grounded summaries and selects more representative images. |
Measuring What Matters Beyond Text: Evaluating Multimodal Summaries by Quality, Alignment, and Diversity (2026.findings-acl)
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| Challenge: | MLLMs have facilitated multimodal summarization with multimodal outputs, but their evaluation is fragmented . MM-Eval integrates assessments of textual quality, cross-modal alignment, and visual diversity . |
| Approach: | They propose a unified evaluation framework that integrates assessments of textual quality, cross-modal alignment, and visual diversity. |
| Outcome: | The proposed framework improves over heuristic aggregation baselines and provides an interpretable, reference-weak framework for comparative evaluation of multimodal summaries. |