MrHiSum: Multimodal Video Summarization Dataset

• MrHiSum is a large-scale dataset for generic video highlight detection and summarization, providing frame-level importance scores aggregated from viewer behavior.
• The extension S-MrHiSum integrates multimodal inputs like generated summary scripts and ASR-based transcripts to enable script-driven video summarization.
• The benchmark supports regression-based learning with dense supervision and algorithmic summary generation via a Knapsack optimization protocol.

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 (Mylonas et al., 7 Oct 2025). 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 (Mylonas et al., 7 Oct 2025).

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 (Mylonas et al., 7 Oct 2025).

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 (Mylonas et al., 7 Oct 2025).

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” (Mylonas et al., 7 Oct 2025).

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 (Mylonas et al., 7 Oct 2025).

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

$$\mathbf{X} = \{\mathbf{x}_n\}_{n=1}^{N}, \qquad \mathbf{x}_n \in \mathbb{R}^D.$$0

Frames, script sentences, and transcript sentences are encoded once, and training then uses the stored embeddings (Mylonas et al., 7 Oct 2025).

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 $$\mathbf{X} = \{\mathbf{x}_n\}_{n=1}^{N}, \qquad \mathbf{x}_n \in \mathbb{R}^D.$$1, and the script–transcript branch produces $$\mathbf{X} = \{\mathbf{x}_n\}_{n=1}^{N}, \qquad \mathbf{x}_n \in \mathbb{R}^D.$$2. Their concatenation is dimension-reduced to

$$\mathbf{X} = \{\mathbf{x}_n\}_{n=1}^{N}, \qquad \mathbf{x}_n \in \mathbb{R}^D.$$3

which is passed to a Transformer-based frame scorer followed by a final linear layer with sigmoid.

For the $$\mathbf{X} = \{\mathbf{x}_n\}_{n=1}^{N}, \qquad \mathbf{x}_n \in \mathbb{R}^D.$$4-th attention head, queries come from visual or transcript embeddings and keys and values come from script embeddings: $$\mathbf{X} = \{\mathbf{x}_n\}_{n=1}^{N}, \qquad \mathbf{x}_n \in \mathbb{R}^D.$$5 Raw attention is

$$\mathbf{X} = \{\mathbf{x}_n\}_{n=1}^{N}, \qquad \mathbf{x}_n \in \mathbb{R}^D.$$6

The weighting mechanism explicitly introduces a cosine-similarity scaling matrix: $$\mathbf{X} = \{\mathbf{x}_n\}_{n=1}^{N}, \qquad \mathbf{x}_n \in \mathbb{R}^D.$$7 and the weighted attention becomes

$$\mathbf{X} = \{\mathbf{x}_n\}_{n=1}^{N}, \qquad \mathbf{x}_n \in \mathbb{R}^D.$$8

The head output is

$$\mathbf{X} = \{\mathbf{x}_n\}_{n=1}^{N}, \qquad \mathbf{x}_n \in \mathbb{R}^D.$$9

with multi-head aggregation

$$\mathbf{f}^{\text{gt}} = \{f_n^{\text{gt}}\}_{n=1}^{N}.$$0

The same formulation applies to script–transcript attention by replacing $$\mathbf{f}^{\text{gt}} = \{f_n^{\text{gt}}\}_{n=1}^{N}.$$1 with $$\mathbf{f}^{\text{gt}} = \{f_n^{\text{gt}}\}_{n=1}^{N}.$$2.

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 $$\mathbf{f}^{\text{gt}} = \{f_n^{\text{gt}}\}_{n=1}^{N}.$$3, dropout 0.5, and L2 weight decay $$\mathbf{f}^{\text{gt}} = \{f_n^{\text{gt}}\}_{n=1}^{N}.$$4. Evaluation follows the original Knapsack summary-generation protocol and reports F1 score, Kendall’s $$\mathbf{f}^{\text{gt}} = \{f_n^{\text{gt}}\}_{n=1}^{N}.$$5, and Spearman’s $$\mathbf{f}^{\text{gt}} = \{f_n^{\text{gt}}\}_{n=1}^{N}.$$6 (Mylonas et al., 7 Oct 2025).

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 (Mylonas et al., 7 Oct 2025).

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

Method	Reported metrics
SD-MVSum	F1 $$\mathbf{f}^{\text{gt}} = \{f_n^{\text{gt}}\}_{n=1}^{N}.$$7, $$\mathbf{f}^{\text{gt}} = \{f_n^{\text{gt}}\}_{n=1}^{N}.$$8, $$\mathbf{f}^{\text{gt}} = \{f_n^{\text{gt}}\}_{n=1}^{N}.$$9
SD-VSum	F1 $\mathbf{f} = \{f_n\}_{n=1}^{N}$0, $\mathbf{f} = \{f_n\}_{n=1}^{N}$1, $\mathbf{f} = \{f_n\}_{n=1}^{N}$2
CLIP-It	F1 $\mathbf{f} = \{f_n\}_{n=1}^{N}$3, $\mathbf{f} = \{f_n\}_{n=1}^{N}$4, $\mathbf{f} = \{f_n\}_{n=1}^{N}$5
A2Summ	F1 $\mathbf{f} = \{f_n\}_{n=1}^{N}$6, $\mathbf{f} = \{f_n\}_{n=1}^{N}$7, $\mathbf{f} = \{f_n\}_{n=1}^{N}$8
CSTA	F1 $\mathbf{f} = \{f_n\}_{n=1}^{N}$9, $d_n$0, $d_n$1
PGL-SUM	F1 $d_n$2, $d_n$3, $d_n$4

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, $d_n$5, and $d_n$6. This suggests that transcripts provide a measurable complementary signal and that cosine-similarity-based weighting contributes more strongly to the final ranking quality (Mylonas et al., 7 Oct 2025).

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 (Mylonas et al., 7 Oct 2025).

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 $d_n$7 across many iterations, emphasizing decomposition by magnification ratios, restricted addition graphs, and submultiplicative $d_n$8-dependence in upper bounds (Petridis, 2011). 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 (Blümlein et al., 2017).

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.