MULSUM: A Multimodal Summarization System with Vis-Aligner and Diversity-Aware Image Selection.
| Authors | Abid Ali et al. |
| Year | 2026 |
| Venue | EACL 2026 |
| Paper | View on ACL Anthology |
| PDF | Download |
Abstract
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Engineering Breakdown
Plain English
MULSUM is a multimodal summarization system that generates summaries from documents containing both text and images. The system introduces two key components: a Vis-Aligner that connects visual content to text context, and a Diversity-Aware Image Selection mechanism that picks the most relevant images to include in the summary rather than including all images indiscriminately.
Key Engineering Insight
The critical innovation is treating image selection as a diversity problem rather than a relevance problem alone — the system learns to pick images that reduce redundancy while maintaining alignment with the text summary, which is a harder optimization problem than simple relevance ranking.
Why It Matters for Engineers
Most production summarization systems today handle text only, even though many documents (reports, articles, news) contain critical visual information. This work directly addresses the engineering challenge of building end-to-end multimodal pipelines that don't naively concatenate modalities but intelligently fuse them, making it practical to deploy better summarization in real document processing systems.
Research Context
Prior multimodal summarization work either ignored images entirely or treated them as secondary features. MULSUM advances the field by explicitly modeling the text-image relationship through the Vis-Aligner and solving the image selection problem, enabling better multimodal document understanding that was previously limited by treating images independently.
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