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arxiv: 2510.05652 · v2 · submitted 2025-10-07 · 💻 cs.CV

SD-MVSum: Script-Driven Multimodal Video Summarization Method and Datasets

Manolis Mylonas , Charalampia Zerva , Evlampios Apostolidis , Vasileios Mezaris This is my paper

Pith reviewed 2026-05-18 08:58 UTC · model grok-4.3

classification 💻 cs.CV
keywords video summarizationmultimodal video summarizationscript-driven summarizationcross-modal attentionsemantic similarityvideo datasets
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The pith

The SD-MVSum method uses a weighted cross-modal attention mechanism to create video summaries that align with a user script by considering both visual and spoken content.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper develops SD-MVSum for script-driven multimodal video summarization. It extends previous work by adding the transcript to the visual content and modeling their separate relations to the script with a new weighted cross-modal attention. This attention uses semantic similarity to highlight the video segments that best fit the script. The authors also enlarge the S-VideoXum and MrHiSum datasets to include the needed multimodal data. Results show the method is competitive with state-of-the-art techniques for both script-based and generic video summarization.

Core claim

SD-MVSum builds on the SD-VSum method by incorporating the audio transcript modality and using a weighted cross-modal attention mechanism that exploits semantic similarity between the script-video pair and the script-transcript pair to promote the parts of the video with highest relevance to the user script. The method and the extended datasets demonstrate competitive performance against other state-of-the-art approaches.

What carries the argument

The weighted cross-modal attention mechanism that exploits semantic similarity between the script and each of the visual and transcript modalities to select relevant video segments.

If this is right

  • Video summaries can better reflect user scripts that refer to dialogue.
  • The approach works across script-driven and generic video summarization tasks.
  • New extended datasets facilitate development of multimodal summarization methods.
  • Competitive results are obtained without additional post-processing or tuning.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • This mechanism might be adapted for other tasks like video question answering where text queries need to match audio and visuals.
  • It highlights the value of semantic matching over simple concatenation of modalities.
  • Testing with diverse, real-world user scripts could show the limits of the similarity-based selection.

Load-bearing premise

The semantic similarity computed by the attention mechanism accurately captures the relevance of video segments to the user script.

What would settle it

An experiment where the full SD-MVSum is compared to versions without the transcript or without the weighting, and the latter perform equally well or better according to standard metrics or human judgment on the datasets.

Figures

Figures reproduced from arXiv: 2510.05652 by Charalampia Zerva, Evlampios Apostolidis, Manolis Mylonas, Vasileios Mezaris.

Figure 1
Figure 1. Figure 1: Overview of the SD-MVSum network architecture. Given an input video, a user script about the content of the summary, and a set [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The processing pipeline in the weighted cross-modal attention mechanism when fusing the visual and the script embeddings. The [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Overview of the processing pipeline for creating the S-MrHiSum dataset. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: An indicative sample from our qualitative analysis. The upper part provides a keyframe-based representation of the content of [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
read the original abstract

In this work, we present a method and two large-scale datasets for Script-Driven Multimodal Video Summarization. The proposed method, SD-MVSum, builds on our earlier SD-VSum method for script-driven video summarization, which considered just the visual content of the video. SD-MVSum takes into account, in addition to the visual modality, the relevance of the user-provided script with the spoken content (i.e., audio transcript) of the video. The dependence between each considered pair of data modalities, i.e., script-video and script-transcript, is modeled using a new weighted cross-modal attention mechanism. This mechanism explicitly exploits the semantic similarity between the paired modalities in order to promote the parts of the full-length video with the highest relevance to the user-provided script. Furthermore, we extend two large-scale datasets for script-driven (S-VideoXum) and generic (MrHiSum) video summarization, to make them suitable for training and evaluation of script-driven multimodal video summarization methods. Experimental comparisons document the competitiveness of the proposed SD-MVSum method against other SotA approaches for script-driven and generic video summarization. Our new method and extended datasets are available at: https://github.com/IDT-ITI/SD-MVSum.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 3 minor

Summary. The manuscript proposes SD-MVSum, a method for script-driven multimodal video summarization that extends the authors' prior SD-VSum work. SD-MVSum incorporates both visual and transcript modalities by modeling pairwise dependencies (script-video and script-transcript) with a new weighted cross-modal attention mechanism that exploits semantic similarity to promote script-relevant segments. The authors extend the S-VideoXum and MrHiSum datasets to support multimodal script-driven summarization and report that SD-MVSum achieves competitive performance against state-of-the-art methods on both script-driven and generic video summarization tasks. Public code and datasets are released.

Significance. If the reported competitiveness holds under rigorous evaluation, the work advances script-driven video summarization by adding a transcript modality fused via semantically motivated attention. The dataset extensions address a resource gap for multimodal settings, and the public code plus direct SotA comparisons constitute clear strengths that support reproducibility and independent verification. This could inform practical systems for personalized or content-aware video summarization.

minor comments (3)
  1. The abstract states that experimental comparisons document competitiveness, but it would improve immediate clarity to name the primary metrics (e.g., F1 or mAP) and the main baseline families used.
  2. In the method description, the precise formulation of the weighting in the cross-modal attention (how semantic similarity scores are normalized and applied) would benefit from an explicit equation or pseudocode block for reproducibility.
  3. When presenting the extended S-VideoXum and MrHiSum datasets, quantitative statistics on the added transcript coverage and any new annotation protocol should be included to allow readers to judge the scale of the multimodal extension.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for their positive and accurate summary of our work on SD-MVSum, including the recognition of the weighted cross-modal attention mechanism, the dataset extensions, and the public release of code and data. We appreciate the recommendation for minor revision and the acknowledgment of the potential impact on practical systems for script-driven summarization.

Circularity Check

0 steps flagged

Minor self-citation to prior SD-VSum; new attention mechanism and experiments supply independent content

full rationale

The manuscript explicitly builds on the authors' earlier SD-VSum for the visual-only case but introduces a distinct weighted cross-modal attention mechanism to model script-transcript relevance in addition to script-video. Competitiveness is demonstrated via direct experimental comparisons against SotA methods on the extended S-VideoXum and MrHiSum datasets, with public code released. No load-bearing step reduces a prediction to a fitted input by construction, no self-definitional loop appears in the architecture description, and the self-citation is not invoked to forbid alternatives or to justify the core multimodal claim. The derivation chain remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The approach relies on standard deep-learning assumptions for attention and multimodal fusion; no explicit free parameters, new axioms, or invented entities are described in the abstract.

axioms (1)
  • standard math Standard assumptions in neural network training and attention mechanisms for semantic similarity computation.
    The weighted cross-modal attention presupposes typical properties of learned embeddings and similarity metrics in multimodal models.

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