---
title: 'MrHiSum: Multimodal Video Summarization Dataset'
url: https://www.emergentmind.com/topics/mrhisum
type: topic
---

# MrHiSum: Multimodal Video Summarization Dataset

Searching arXiv for relevant papers on "MrHiSum" and closely related terms.
MrHiSum is a large-scale benchmark for video highlight detection and summarization that was originally designed for the generic, rather than user- or script-driven, setting. In the formulation summarized by "SD-MVSum: Script-Driven Multimodal Video Summarization Method and Datasets," MrHiSum provides frame-level importance supervision derived from aggregated YouTube viewing behavior, while its extension, **S-MrHiSum**, adds summary scripts and spoken-content transcripts in order to support multimodal script-driven video summarization [2510.05652]. The designation also appears in unrelated expository uses in additive combinatorics and symbolic summation, but in multimedia research it denotes the dataset and its multimodal extension.

## 1. Original benchmark and task definition

MrHiSum was introduced by Sul et al. as “a large-scale dataset for video highlight detection and summarization.” Its original purpose was to support **generic** video summarization and highlight detection, with no user query, script, or textual description involved at training or testing time. Ground truth is defined through frame-level importance scores, also called “highlight labels,” that reflect collective viewer preferences rather than prompt-conditioned relevance [2510.05652].

The original benchmark addresses two closely related problems: highlight detection and generic video summarization. In this setting, a model ranks frames or segments according to importance, and a summary is subsequently formed from those scores. The design goal is not to tailor summaries to a specific textual request, but to identify the most “highlight-worthy” portions of a video according to aggregated audience behavior.

At the dataset level, MrHiSum contains **31,892 videos** spanning **3,509 distinct domains**. Videos are drawn from the YouTube-8M dataset, have maximum length **up to 5 minutes**, and average length **3.3 minutes**. The original release is **video only**: it provides no transcripts, scripts, or text descriptions. Each sample has a single annotation set consisting of frame-level importance scores. These scores were computed by aggregating the viewing preferences of **at least 50,000 viewers** per YouTube video, and they serve as the supervisory signal for both highlight detection and summary construction [2510.05652].

## 2. Representation, supervision, and summary construction

In the notation used for MrHiSum and its extension, a video is sampled at **1 frame per second** and treated as a sequence of \(N\) frames. Frame-level visual embeddings are written as
\[
\mathbf{X} = \{\mathbf{x}_n\}_{n=1}^{N}, \qquad \mathbf{x}_n \in \mathbb{R}^D.
\]
The original MrHiSum labels are frame-level importance scores
\[
\mathbf{f}^{\text{gt}} = \{f_n^{\text{gt}}\}_{n=1}^{N}.
\]
This makes the dataset suitable for dense supervision of temporal importance estimation rather than only binary keyshot prediction [2510.05652].

A central feature of MrHiSum is that summaries are not distributed as free-text descriptions or explicit key-shot indices. Instead, the dataset provides a **continuous importance score per frame**, and the ground-truth summary is derived algorithmically by solving a Knapsack problem under a time budget. Given predicted scores \(\mathbf{f} = \{f_n\}_{n=1}^{N}\), frame durations \(d_n\) (here 1 second per frame), and budget \(B\), summary generation is formulated as
\[
\text{select } S \subseteq \{1,\dots,N\} \text{ such that } \sum_{n\in S} d_n \le B,\quad \sum_{n\in S} f_n \text{ is maximized.}
\]
This summary-construction protocol, adopted from the original benchmark, is preserved in the later multimodal extension.

The use of dense, graded importance labels is significant because it supports regression-based learning. In the SD-MVSum formulation, the model predicts frame-level scores and is trained with MSE against the inherited MrHiSum importance annotations. This preserves the original notion of highlight salience even after scripts and transcripts are added.

## 3. Extension to S-MrHiSum

The original benchmark cannot support script-driven multimodal summarization because it lacks textual conditioning signals and spoken-content transcripts. To address that limitation, the SD-MVSum work introduces **S-MrHiSum**, an extended version of MrHiSum designed for “multimodal script-driven video summarization” [2510.05652].

| Property | MrHiSum | S-MrHiSum |
|---|---|---|
| Samples | 31,892 videos | 29,918 videos |
| Modalities | video | video, text, transcripts |
| Task | generic highlight detection & summarization | multimodal script-driven summarization |

S-MrHiSum preserves the **3,509 domains** of the original dataset but contains **29,918 videos**, a smaller subset than the original 31,892. The paper does not specify the precise cause of this reduction, though it is associated with the multimodal processing pipeline. Each S-MrHiSum sample includes video, a textual **summary script**, and ASR-based transcripts. It retains one annotation set per sample and uses the original frame-level importance scores to derive ground-truth summaries and supervise training.

The extension pipeline has two branches. In the first, the ground-truth summary determined from highlight labels and Knapsack is treated as a “summary video,” sampled at **1 frame per second**, and passed to **LLaVA-NeXT-Video-7B** with the prompt `"describe the important scenes in this video"`. The model produces a textual description of the summary of **up to 200 tokens**. The implementation uses **4-bit quantization** to reduce computation cost. This generated description becomes the summary script.

In the second branch, audio is extracted from the full-length video and processed with **Silero VAD** to detect speech segments. These segments are transcribed with **Whisper Turbo**. Because the multimodal encoder is trained on English text, non-English transcripts are translated into English using **NLLB**. The result is a set of timestamped transcript sentences
\[
\tilde{\mathbf{T}} = \{\mathbf{t}_k\}_{k=1}^{K},
\]
which are then expanded frame-wise to
\[
\mathbf{T} = \{\mathbf{t}_n\}_{n=1}^{N}
\]
by repeating each transcript embedding across the frames it covers temporally [2510.05652].

A common misconception is that S-MrHiSum provides human-authored scripts. It does not: the scripts are generated automatically from the highlight-based summary video. Another common misconception is that MrHiSum was originally multimodal. It was originally **video only**; the script and transcript modalities are introduced only in S-MrHiSum.

## 4. Formal multimodal problem and SD-MVSum architecture

In the script-driven multimodal setting, a sample consists of video frames, script sentences, and timestamped transcript sentences. After CLIP encoding, the representations are
\[
\mathbf{X} = \{\mathbf{x}_n\}_{n=1}^{N},\qquad
\mathbf{Y} = \{\mathbf{y}_m\}_{m=1}^{M},\qquad
\mathbf{T} = \{\mathbf{t}_n\}_{n=1}^{N}.
\]
For S-MrHiSum, the implementation uses **CLIP ViT-B/32 from HuggingFace** with embedding dimension
\[
D = 512.
\]
Frames, script sentences, and transcript sentences are encoded once, and training then uses the stored embeddings [2510.05652].

SD-MVSum models the dependence between script and video, and between script and transcript, with two **weighted cross-modal attention** modules. The script–video branch produces \(\mathbf{Z}_v = \{\mathbf{z}_{v,n}\}_{n=1}^{N}\), and the script–transcript branch produces \(\mathbf{Z}_t = \{\mathbf{z}_{t,n}\}_{n=1}^{N}\). Their concatenation is dimension-reduced to
\[
\mathbf{Z} = \{\mathbf{z}_n\}_{n=1}^{N},
\]
which is passed to a Transformer-based frame scorer followed by a final linear layer with sigmoid.

For the \(h\)-th attention head, queries come from visual or transcript embeddings and keys and values come from script embeddings:
\[
\mathbf{Q}_h = \{\mathbf{q}_n\}_{n=1}^{N}, \qquad
\mathbf{K}_h = \{\mathbf{k}_m\}_{m=1}^{M}, \qquad
\mathbf{V}_h = \{\mathbf{v}_m\}_{m=1}^{M}.
\]
Raw attention is
\[
\mathbf{A}^h = \mathbf{Q}_h \mathbf{K}_h^\top.
\]
The weighting mechanism explicitly introduces a cosine-similarity scaling matrix:
\[
\mathbf{X}_n = \text{L2}(\mathbf{X}), \qquad
\mathbf{Y}_n = \text{L2}(\mathbf{Y}), \qquad
\mathbf{S} = \mathbf{X}_n \mathbf{Y}_n^\top,
\]
and the weighted attention becomes
\[
\hat{\mathbf{A}}^h = \mathbf{A}^h \odot \mathbf{S}.
\]
The head output is
\[
\mathbf{Z}_v^h = \text{softmax}(\hat{\mathbf{A}}^h)\mathbf{V}_h,
\]
with multi-head aggregation
\[
\mathbf{Z}_v = \text{Concat}(\mathbf{Z}_v^1,\dots,\mathbf{Z}_v^H)\mathbf{W}^o + pe.
\]
The same formulation applies to script–transcript attention by replacing \(\mathbf{X}\) with \(\mathbf{T}\).

Training on S-MrHiSum uses **MSE** between predicted frame scores and the inherited ground-truth importance scores. The reported optimization setup is: **50 epochs**, **batch size 64**, **Adam**, learning rate \(5\cdot 10^{-5}\), **dropout 0.5**, and **L2 weight decay \(10^{-4}\)**. Evaluation follows the original Knapsack summary-generation protocol and reports **F1 score**, **Kendall’s \(\tau\)**, and **Spearman’s \(\rho\)** [2510.05652].

## 5. Empirical role in multimodal video summarization

S-MrHiSum occupies a distinct position among summarization datasets. Compared with **S-VideoXum**, it is larger in video count but provides only **one ground-truth summary and one script per video**, whereas S-VideoXum provides **10 ground-truth summaries** and **10 textual descriptions** per video. S-VideoXum uses human-written summary descriptions, while S-MrHiSum uses LLM-generated descriptions of highlight-based summaries. S-MrHiSum is built from a dataset originally meant for highlight detection with large-scale frame importance labels, rather than from a dataset originally meant for cross-modal summarization [2510.05652].

On S-MrHiSum, the reported comparison against script-driven and generic baselines is as follows.

| Method | Reported metrics |
|---|---|
| SD-MVSum | F1 \(= 58.6\), \(\tau = 0.193\), \(\rho = 0.258\) |
| SD-VSum | F1 \(= 58.2\), \(\tau = 0.170\), \(\rho = 0.230\) |
| CLIP-It | F1 \(= 56.5\), \(\tau = 0.105\), \(\rho = 0.139\) |
| A2Summ | F1 \(= 58.0\), \(\tau = 0.176\), \(\rho = 0.248\) |
| CSTA | F1 \(= 56.1\), \(\tau = 0.192\), \(\rho = 0.269\) |
| PGL-SUM | F1 \(= 55.7\), \(\tau = 0.073\), \(\rho = 0.108\) |

The ablation study on S-MrHiSum reports three configurations: full SD-MVSum with script, visual, and transcript inputs plus weighted attention; a variant without transcripts; and a variant without dynamic scaling in the attention mechanism. Their respective metrics are **58.6 / 0.193 / 0.258**, **58.4 / 0.178 / 0.243**, and **58.0 / 0.126 / 0.220** for F1, \(\tau\), and \(\rho\). This suggests that transcripts provide a measurable complementary signal and that cosine-similarity-based weighting contributes more strongly to the final ranking quality [2510.05652].

The broader significance of MrHiSum within summarization research lies in its dense frame-level supervision. The dataset directly encodes **collective viewer preferences** through highlight labels, and S-MrHiSum preserves that property while enabling models to condition on generated scripts and spoken content.

## 6. Limitations, ambiguity of the name, and other technical uses

Several limitations are explicit or implied in the S-MrHiSum construction. First, the summary scripts are generated by **LLaVA-NeXT-Video-7B** rather than written by humans, so the textual conditioning signal reflects LLM-generated descriptions of highlights. Second, transcripts depend on **Whisper Turbo** and, where necessary, **NLLB**, so ASR and translation errors can propagate into the script–transcript alignment. Third, the reduction from **31,892** original videos to **29,918** processed videos indicates a selection effect associated with multimodal preprocessing. Fourth, S-MrHiSum provides only **one script per video**, which does not capture diversity in user preferences. Fifth, the evaluation methodology inherits the original Knapsack-based time-budget protocol, and other budgets or user-specific constraints are not explored [2510.05652].

The name **MrHiSum** is also ambiguous across technical writing. In one additive-combinatorics exposition built around Petridis’s results on higher sumsets, “MrHiSum” is used as the name of a method or framework for controlling \(|A+hB|\) across many iterations, emphasizing decomposition by magnification ratios, restricted addition graphs, and submultiplicative \(h\)-dependence in upper bounds [1101.5001]. In a separate symbolic-summation exposition based on refined holonomic summation and the **RhoSum** package, “MrHiSum”-type systems are described as high-level multi-summation systems combining RΠΣ-extensions, higher-order linear difference-ring extensions, refined parameterized telescoping, and recurrence discovery for nested sums in particle-physics calculations [1706.03677].

These latter uses are conceptually unrelated to the multimedia benchmark. In contemporary video summarization research, MrHiSum refers to the large-scale dataset for generic highlight detection and summarization, and S-MrHiSum denotes its script-driven multimodal extension.

Source: https://www.emergentmind.com/topics/mrhisum