Enterprise AI Analysis
Cut to the Chase: Training-free Multimodal Summarization via Chain-of-Events
Multimodal Summarization (MMS) aims to generate concise textual summaries by understanding and integrating information across videos, transcripts, and images. However, existing approaches still suffer from three main challenges: (1) reliance on domain-specific supervision, (2) implicit fusion with weak cross-modal grounding, and (3) flat temporal modeling without event transitions. To address these issues, we introduce CoE, a training-free MMS framework that performs structured reasoning through a Chain-of-Events guided by a Hierarchical Event Graph (HEG). The HEG encodes textual semantics into an explicit event hierarchy that scaffolds cross-modal grounding and temporal reasoning. Guided by this structure, CoE localizes key visual cues, models event evolution and causal transitions, and refines outputs via lightweight style adaptation for domain alignment. Extensive experiments on eight diverse datasets demonstrate that CoE consistently outperforms state-of-the-art video CoT baselines, achieving average gains of +3.04 ROUGE, +9.51 CIDEr, and +1.88 BERTScore, highlighting its robustness, interpretability, and cross-domain generalization. Our code is available at https://github.com/youxiaoxing/CoE.
Executive Impact
CoE demonstrates significant advancements in multimodal summarization, offering improved accuracy and adaptability without the need for extensive training.
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Deep Analysis & Enterprise Applications
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CoE Overview
HEG Construction
Cross-modal Grounding
Event Evolution
Summary Generation
CoE (Chain-of-Events) is a training-free MMS framework that performs structured multimodal reasoning through a Hierarchical Event Graph (HEG). It addresses key limitations of existing MMS models by enabling accurate, interpretable, and domain-adaptive summarization without fine-tuning.
The Hierarchical Event Graph (HEG) explicitly encodes textual semantics into event hierarchies (Global, Sub-Event, Entity-Relation layers). It serves as a semantic backbone for zero-shot multimodal reasoning and summary generation, guiding cross-modal grounding and temporal reasoning.
Cross-modal Spatial Grounding (CSG) uses the HEG to guide video interpretation, aligning clips with sub-event anchors and grounding entity-relation triples to produce visually supported subgraphs. This enhances cross-modal grounding with relational semantics for fine-grained correspondences.
Event Evolution Reasoning (EER) traces how sub-events emerge, persist, and transition across aggregated temporal segments. By analyzing subgraph changes, it captures causal and temporal dependencies, yielding coherent long-horizon summarization for dynamic narrative understanding.
Domain-adaptive Summary Generation (DSG) synthesizes event trajectories into an initial summary and refines it via lightweight style adaptation. It adjusts linguistic tone and formality across diverse domains, ensuring robustness without domain-specific supervision while preserving factual accuracy.
+9.51
Average CIDEr Gain Over SOTA Baselines
CoE Framework Breakdown
HEG Construction
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Cross-modal Spatial Grounding (CSG)
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Event Evolution Reasoning (EER)
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Domain-adaptive Summary Generation (DSG)
| Feature | Supervised MMS (MLASK/MMSum) | CoE (Chain-of-Events) |
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| Cross-modal Grounding |
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| Temporal Modeling |
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| Generalization |
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| Interpretability |
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Case Study: Robust Temporal Coherence
By modeling event evolution and entity transitions, CoE constructs temporally coherent narratives that closely resemble human-written summaries. This moves beyond surface-level scene description toward event-centric narratives, demonstrated qualitatively in various domains including news and TV scripts.
Calculate Your Potential ROI with CoE
Understand the potential efficiency gains and cost savings by integrating CoE into your enterprise workflows.
Estimated Annual Savings
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CoE Implementation Roadmap
A clear path to integrate Chain-of-Events into your enterprise, ensuring a smooth transition and maximum impact.
Phase 1: Initial Assessment & Data Integration
Evaluate existing data sources and integrate video-text pairs into the CoE framework for initial testing.
Phase 2: Custom HEG & Grounding Refinement
Tailor the Hierarchical Event Graph for specific domain semantics and refine cross-modal grounding for optimal accuracy.
Phase 3: Event Trajectory & Narrative Tuning
Configure event evolution reasoning to capture unique narrative structures and causal transitions relevant to your content.
Phase 4: Style Adaptation & Deployment
Implement lightweight style adaptation to align summaries with brand guidelines and deploy CoE for real-world applications.
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