Minimal Clips, Maximum Salience: Long Video Summarization via Key Moment Extraction
Proposes lightweight clip selection combined with large language models to extract key moments, achieving near-reference summary quality with less than 6% video content.
Key Findings
Methodology
This paper introduces a multimodal long video summarization framework that segments videos into 20-second clips, generates concise visual descriptions via lightweight captioning models, and employs a large language model (LLM) to select the K most relevant clips. The selected clips are integrated into a screenplay-like document combining dialogue and visual cues, which is then summarized into a comprehensive multimodal summary. The approach leverages zero-shot capabilities of models like Qwen2.5-Omni-3B and Gemini 2.5 Flash-Lite, focusing on visual salience to filter out redundant information, thus maintaining low computational cost while capturing essential visual moments. Evaluation on the MovieSum dataset demonstrates that less than 6% of the video content suffices to produce summaries comparable to those derived from full human-annotated screenplays, significantly outperforming random clip selection in relevant visual information capture.
Key Results
- The method achieves ROUGE-1 scores of 47.28 and surpasses random clip baselines in visual recall by over 20%, with the ability to retrieve key visual moments effectively using only a small fraction of the total video (~6%).
- Recall@K evaluations show consistent superiority across K=25, 50, 75, especially when using stronger captioning models, indicating robust selection of salient clips.
- Incorporating high-quality captions from gold screenplays boosts recall metrics substantially, confirming the importance of caption quality in visual fact retrieval and summary accuracy.
Significance
This work addresses the critical challenge of long video understanding by proposing a low-cost, high-efficiency clip selection strategy that preserves essential multimodal information. It enables scalable, accurate summarization suitable for applications in entertainment, surveillance, and education, where processing entire videos is computationally prohibitive. The approach demonstrates that a small, well-chosen subset of video content can yield near-complete multimodal summaries, paving the way for practical deployment of intelligent video analysis systems.
Technical Contribution
The core innovation lies in combining lightweight captioning with large-scale zero-shot models for salient clip detection, introducing a novel visual salience-based filtering mechanism. The pipeline integrates automatic reference clip extraction, multimodal content fusion, and summary generation, balancing efficiency with high-quality output. This approach advances the state-of-the-art by reducing computational costs while maintaining or exceeding the performance of more resource-intensive methods, offering a scalable solution for long video summarization.
Novelty
This research is the first to systematically leverage lightweight visual descriptions and large language models for zero-shot salient clip selection in multimodal long video summarization. Unlike prior work relying on full video analysis or uniform sampling, it emphasizes visual salience and multimodal content relevance, filling a gap in efficient content filtering for large-scale video understanding. Its integration of automatic reference clip derivation and multimodal fusion constitutes a significant methodological breakthrough.
Limitations
- The effectiveness heavily depends on the quality of generated captions; poor descriptions can lead to missed key moments, especially in complex scenes or with ambiguous dialogue.
- Fixed clip length (20 seconds) may not adapt well to videos with variable scene durations, requiring future dynamic segmentation strategies.
- Model generalization to non-visual or highly diverse content remains limited, and the approach's scalability to extremely long or multi-scene videos needs further validation.
Future Work
Future directions include integrating multimodal features such as audio and motion cues to enhance salience detection, developing adaptive clip segmentation algorithms, and exploring fine-tuning of models on domain-specific datasets. Additionally, efforts will focus on reducing model inference costs further and extending applicability to real-time video summarization in streaming scenarios.
AI Executive Summary
In an era where video content proliferates at an unprecedented rate, efficiently extracting meaningful summaries from long videos has become a pressing challenge. Traditional approaches often rely on exhaustive analysis or uniform sampling, which are computationally expensive and prone to missing critical moments. Addressing this, the present study introduces a novel framework that combines lightweight visual descriptions with large language models to identify and select the most salient video segments. The core idea is to segment videos into manageable 20-second clips, generate concise captions using resource-efficient models like Qwen2.5-Omni-3B, and then leverage the zero-shot capabilities of advanced LLMs to filter out the most visually relevant clips. These clips, constituting less than 6% of the total video, serve as the foundation for constructing a comprehensive multimodal summary that integrates dialogue and visual cues. Extensive experiments on the MovieSum dataset demonstrate that this approach achieves summary quality close to that derived from full human-annotated screenplays, with ROUGE-1 scores reaching 47.28 and visual fact recall surpassing baseline methods by over 20%. The method's efficiency stems from its strategic filtering mechanism, which drastically reduces computational load while maintaining high information fidelity. This breakthrough opens new avenues for scalable, real-time long video understanding, with broad applications spanning entertainment, surveillance, and education. Future work will focus on multimodal feature integration, adaptive segmentation, and real-time deployment, further pushing the boundaries of automated video summarization technology.
Deep Analysis
Background
The rapid growth of long-form video content has driven extensive research into automated understanding and summarization techniques. Early methods focused on text-based summarization using neural sequence models like LSTMs and Transformers, which achieved success in news and scientific articles. With the advent of multimodal learning, models such as VideoBERT and Video-Language Transformers integrated visual, audio, and textual data to generate richer summaries. However, these approaches often require significant computational resources, especially when processing hours-long videos. Content selection strategies, including keyframe extraction and scene segmentation, have been proposed to improve efficiency, but they typically lack the ability to effectively fuse multimodal cues or operate in a zero-shot setting. The MovieSum dataset has facilitated progress by providing aligned video, dialogue, and screenplay annotations, but scalable, low-cost methods for long video summarization remain an open challenge.
Core Problem
Despite advances, existing methods struggle with balancing computational efficiency and summary completeness. Uniform sampling or full-video analysis leads to high costs and often misses critical visual moments. The core challenge is to develop a method that can accurately identify and extract the most informative segments—particularly those with salient visual cues—without exhaustive processing. This is crucial for practical applications such as content retrieval, personalized recommendations, and automated content generation, where resource constraints demand efficient solutions. Moreover, in multimodal settings, the redundancy between modalities complicates content filtering, requiring strategies that can discern visual information that cannot be inferred from dialogue alone.
Innovation
The key innovation is the integration of lightweight captioning models with large language models for zero-shot salient clip selection. First, videos are segmented into 20-second clips, and each is described using a fast, resource-efficient captioning model like Qwen2.5-Omni-3B. These captions serve as a compact representation of visual content. Next, an LLM evaluates all captions to identify the K most visually salient clips, leveraging its understanding of multimodal relevance without task-specific training. This process filters out redundant or irrelevant segments, focusing computational resources on the most informative moments. The selected clips are then embedded into a screenplay-like document, combining dialogue and visual descriptions, which is summarized into a final multimodal summary. This approach balances low cost with high relevance, enabling scalable long video understanding.
Methodology
- �� 视频划分：将长视频切割为连续的20秒短片。• 轻量字幕生成：用Qwen2.5-Omni-3B模型快速描述每个短片内容。• 大模型筛选：将所有字幕输入大模型（如Qwen2.5-Omni或Gemini 2.5 Flash-Lite），筛选出包含关键视觉信息的K个片段。• 视觉突显分析：基于字幕内容识别视觉上突出的瞬间。• 剧本构建：将筛选片段描述插入对话文本，形成多模态剧本。• 摘要生成：用大模型对剧本进行总结，输出多模态内容丰富的摘要。• 参考片段提取：通过自动推导的参考片段作为评估基准，验证筛选效果。
Experiments
在MovieSum数据集上，比较随机、静音和筛选片段的召回率（Recall@K），使用ROUGE、METEOR和MFACTSUM指标评估摘要质量。不同K值（25、50、75）验证筛选策略的优越性。引入更强的字幕模型后，筛选效果显著提升。人工评估确认筛选片段的代表性。多模型对比验证筛选在捕获视觉信息和生成多模态摘要方面的有效性。结果显示，少于6%的视频内容即可实现接近全片的摘要效果，验证了筛选策略的实用性。
Results
筛选片段占比不足6%，即可获得与完整剧本相似的摘要，ROUGE-1达47.28，视觉事实召回率比随机方法高出20%以上。筛选在不同K值表现优异，尤其在K=25和50时效果更明显。采用更强字幕描述模型后，召回率提升，验证描述质量的重要性。人工评估显示筛选片段与人工参考高度一致，证明方法的有效性。整体来看，该策略在保持低成本的同时，显著提升了多模态信息的捕获能力。
Applications
该方法适用于影视内容自动摘要、视频检索、内容推荐等场景，依赖于视频结构划分和字幕信息。可广泛应用于电影、纪录片、监控录像等领域，帮助用户快速获取关键信息。未来结合多模态特征，将实现更智能的内容理解与个性化推荐，推动视频智能分析产业升级。
Limitations & Outlook
目前方法依赖字幕描述的准确性，描述不完整或偏差会影响筛选效果。固定片段长度（20秒）可能不适应不同视频类型，需引入动态划分机制。模型在极长或内容复杂的视频中的泛化能力有限，未来需优化算法以适应多样场景。模型推理成本虽低，但在大规模应用中仍需硬件支持，未来应提升效率。
Plain Language Accessible to non-experts
想象你在整理一堆长长的录像带，想找出最精彩、最重要的瞬间。传统方法就像是把每一段都看一遍，既费时间又容易漏掉重点。现在，有个聪明的助手会先用简单的描述告诉你每段录像的内容，然后用一个超级厉害的机器人帮你挑出那些最精彩、最关键的部分。这样一来，你只需要看几段，就能快速了解整个故事的精髓。这种方法既省时又能确保不漏掉重要内容，非常适合处理长时间的视频内容。
ELI14 Explained like you're 14
想象你在看一部很长的电影，但你没有时间全部看完。于是，你的朋友帮你挑出一些最精彩、最重要的片段，只告诉你这些片段的内容。这个朋友先用简单的话描述每个片段，然后用一个超级聪明的机器人帮你挑出那些最有趣、最关键的部分。这样，你只看几段，就能大致明白整个故事的核心内容。这不仅节省时间，还能让你快速抓住重点，特别适合在信息太多的时代快速了解长视频的内容。
Abstract
Vision-Language Models (VLMs) are able to process increasingly longer videos. Yet, important visual information is easily lost throughout the entire context and missed by VLMs. Also, it is important to design tools that enable cost-effective analysis of lengthy video content. In this paper, we propose a clip selection method that targets key video moments to be included in a multimodal summary. We divide the video into short clips and generate compact visual descriptions of each using a lightweight video captioning model. These are then passed to a large language model (LLM), which selects the K clips containing the most relevant visual information for a multimodal summary. We evaluate our approach on reference clips for the task, automatically derived from full human-annotated screenplays and summaries in the MovieSum dataset. We further show that these reference clips (less than 6% of the movie) are sufficient to build a complete multimodal summary of the movies in MovieSum. Using our clip selection method, we achieve a summarization performance close to that of these reference clips while capturing substantially more relevant video information than random clip selection. Importantly, we maintain low computational cost by relying on a lightweight captioning model.