Prompts to Summaries: Zero-Shot Language-Guided Video Summarization with Large Language and Video Models

This paper introduces "Prompts-to-Summaries," a novel zero-shot framework that leverages large language models to convert video-language model captions into user-guided video summaries without training data, achieving competitive performance on standard benchmarks while outperforming prior unsupervised methods.

The Gist

Imagine you have a massive library of video files—thousands of hours of footage from your vacation, security cameras, or YouTube. You want to find the best moments, but watching everything is impossible. Usually, to make a "highlight reel," you need a computer program that has been trained on thousands of examples of what humans consider "good" summaries. But what if you want a summary for a video the computer has never seen before, or you want to ask it to "show me only the funny dog moments" or "skip the boring parts"?

This paper introduces a new tool called "Prompts-to-Summaries." Think of it as a smart, zero-training video editor that works like a team of two specialized assistants: a Visual Observer and a Storyteller Critic.

Here is how it works, broken down into simple steps:

1. The Visual Observer (The VideoLM)

First, the system looks at the video. Since computers can't read a 2-hour video all at once (it's too much data), the Visual Observer chops the video into small, logical chunks called "scenes."

• The Analogy: Imagine a film editor watching a raw tape and cutting it into individual scenes: "The car chase," "The dinner party," "The rainstorm."
• The Magic: This observer doesn't just cut; it writes a short caption for every scene, like a movie subtitle. "A man is running with a dog in the park."

2. The Storyteller Critic (The LLM)

Next, the system takes all those captions and hands them to a Storyteller Critic (a Large Language Model, like the AI behind this chat).

• The Job: You give the Critic a specific instruction (a "prompt"). For example: "Make a summary focusing on the dog, but ignore the running."
• The Action: The Critic reads every scene caption and rates them on a scale of 1 to 100. It asks itself: "Does this scene fit the user's request? Is it important to the whole story?"
• The Result: The Critic creates a scorecard. Scenes with dogs get high scores; scenes with just running get low scores.

3. The Smooth Transition (The Glue)

If the Critic just picked the highest-scoring scenes, the video might jump around weirdly (e.g., from a park to a kitchen and back).

• The Fix: The system uses a "smoothing" technique. It treats the scores like a gentle wave rather than a jagged cliff. If a scene is important, the frames right before and after it get a little boost in importance too. This ensures the final video flows naturally, like a well-edited movie rather than a slideshow.

4. The Final Cut

Finally, the system stitches together the highest-scoring frames into a short, cohesive video.

• The Outcome: You get a custom highlight reel that fits your specific request, created without the computer ever needing to be "taught" with a massive dataset first.

Why is this a Big Deal?

• No Training Required: Most video editors are like students who have to study for years on specific textbooks (datasets) before they can do a job. This new method is like a genius who can read any book instantly. It works on any video, from a cooking show to a security camera, without needing prior practice.
• You are the Director: You can ask for anything. "Show me only the parts where people are laughing," or "Remove any violence." The system understands natural language, not just code.
• Beating the Experts: Surprisingly, this "no-training" method actually performs better than many complex, training-heavy methods that have been studied for years.

The "Secret Sauce"

The authors also created a new test called VidSum-Reason. Imagine asking a computer, "Show me the parts where the character realizes they are lost." This requires the AI to understand feelings and logic, not just spot a "person" or a "car." This new tool is one of the first to handle these deep, thinking-based requests successfully.

In short: This paper gives us a universal video summarizer that listens to your voice, understands your intent, and edits your videos instantly, all without needing to be trained on a mountain of data first. It turns the chaotic flood of video data into a personalized, readable story.

Technical Summary

1. Problem Statement

The exponential growth of video data has created a critical need for flexible summarization tools that can operate without extensive training data. Existing methods face two primary limitations:

• Data Dependency: Most supervised and unsupervised methods rely heavily on domain-specific training datasets, limiting their ability to generalize to new video types or domains.
• Lack of User Control: Current approaches generally produce generic summaries and cannot accommodate specific user intents expressed in natural language (e.g., "highlight violent scenes" or "focus on emotional moments").

The authors define the goal as developing a zero-shot, text-queryable video summarization algorithm that takes a video sequence and an optional natural language query to produce a concise, relevant skim without any task-specific training, fine-tuning, or access to ground-truth annotations.

2. Methodology

The proposed framework, Prompts-to-Summaries, orchestrates pre-trained Video-Language Models (VideoLMs) and Large Language Models (LLMs) through a four-stage pipeline:

A. Scene Detection and Refinement

• Initial Detection: The video is segmented into shots using PySceneDetect with a dynamic thresholding mechanism. Instead of a fixed threshold, the system analyzes the rate of change in scene counts across a range of thresholds to find the optimal point where noise-driven fragments disappear but true boundaries remain.
• Refinement: Short scenes (likely noise) are merged with neighbors based on visual embedding similarity (using CLIP or DINO embeddings) to ensure semantic coherence.

B. Scene Description Generation

• VideoLM Captioning: A pre-trained VideoLM generates textual descriptions for each detected scene and a holistic description for the entire video.
• Memory-Efficient Batching: To handle long videos, frames are sampled and processed in batches. The system enforces consistent phrasing (e.g., "The video continues...") between batches to maintain temporal and semantic coherence in the generated text.

C. Scene-Level Importance Scoring (LLM Judge)

• Prompt Engineering: An LLM acts as a judge to assign importance scores (1–100) to each scene.
• Contextual Reasoning: The prompt includes the scene description, the full video description, and an optional user query.
• Rubric-Driven Scoring: The LLM is instructed to act as an "objective evaluator" with a specific rubric. It is biased to reserve high scores (80+) only for scenes that drive the core narrative, preventing score inflation.
• User Guidance: If a user query is provided, the LLM adjusts scores based on alignment with the query (e.g., up-weighting scenes matching "food" and down-weighting irrelevant ones).

D. Frame-Level Scoring and Propagation

To convert scene scores into frame-level importance, the system employs:

1. Temporal Smoothing: A cosine interpolation function smooths scores across scene boundaries to avoid abrupt jumps.
2. Intra-Scene Weighting: Within each scene, frames are weighted based on two metrics:
   • Consistency: How uniformly frames belong to the same visual pattern (via K-Means clustering).
   • Uniqueness: How distinct a segment is from the mean of its frames (novelty).
   • These are combined using a parameter $\sigma$, which is dynamically adjusted based on video duration.
3. Fusion: The final frame score is the product of the smoothed scene score and the intra-scene frame weight, followed by normalization within the scene.

3. Key Contributions

1. First Zero-Shot, Text-Guided Framework: The paper introduces the first pipeline that generates video summaries conditioned on natural language queries without any training data, annotations, or fine-tuning.
2. State-of-the-Art (SOTA) Performance: The method achieves SOTA results among unsupervised methods on standard benchmarks (SumMe and TVSum) and performs competitively with supervised methods on the Query-Focused Video Summarization (QFVS) benchmark.
3. VidSum-Reason Dataset: The authors release a new benchmark dataset featuring long-tailed concepts and multi-step reasoning queries (e.g., "filter violence," "prioritize emotional shifts") to evaluate fine-grained, intent-aware summarization.
4. Comprehensive Ablation Studies: The paper systematically analyzes the impact of image encoders (CLIP vs. DINO), normalization strategies, prompt designs, and visual context (local vs. global) on performance.

4. Experimental Results

• Standard Summarization (SumMe & TVSum):
  • Achieved 56.84% F1 on SumMe and 62.22% F1 on TVSum.
  • Outperformed all existing unsupervised, zero-shot, and few-shot methods.
  • Matched or closely rivaled leading supervised methods (e.g., CLIP-It) despite having no training data.
• Query-Focused Summarization (QFVS):
  • Achieved an average F1 of 53.42%, surpassing four classical supervised baselines and coming within 1 F1 point of the fully supervised, data-hungry CLIP-It (ResNet).
  • Demonstrated the ability to adapt summaries to diverse user queries (e.g., focusing on "Chairs/Trees" vs. "Food/Hands").
• VidSum-Reason Benchmark:
  • The model achieved a mean F1 of 43.4% across four query complexity levels (Standard, Special Attribute, Reasoning, Reasoning + General Knowledge), significantly outperforming a random baseline (34.56%).
  • Showed particular strength in reasoning-heavy queries requiring temporal logic and external knowledge.

5. Significance and Impact

• Paradigm Shift: The work demonstrates that pre-trained multi-modal models, when orchestrated with principled prompting and score propagation, can replace the need for massive annotated datasets in video summarization.
• Generalizability: By operating in a zero-shot, domain-agnostic setting, the framework is highly adaptable to unseen video types and user needs, solving the generalization issues of supervised models.
• Scalability: The memory-efficient batching strategy allows the pipeline to process videos ranging from minutes to hours (tested on 5-hour videos) on a single GPU.
• Future Direction: It establishes a new baseline for "reasoning-based" video understanding, moving beyond simple object detection to complex intent interpretation and temporal reasoning.

In conclusion, Prompts-to-Summaries proves that large language models can effectively serve as "scene judges" to convert raw video captions into high-quality, user-controllable summaries, offering a powerful, training-free alternative to traditional deep learning approaches.