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arxiv: 2604.10024 · v1 · submitted 2026-04-11 · 💻 cs.CV · cs.AI· cs.LG

LVSum: A Benchmark for Timestamp-Aware Long Video Summarization

Alkesh Patel , Melis Ozyildirim , Ying-Chang Cheng , Ganesh Nagarajan This is my paper

Pith reviewed 2026-05-10 15:47 UTC · model grok-4.3

classification 💻 cs.CV cs.AIcs.LG
keywords long video summarizationtemporal reasoningmultimodal large language modelsbenchmark datasettimestamp alignmentvideo understandingMLLM evaluation
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The pith

Current multimodal large language models struggle with temporal accuracy in long video summaries.

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

The authors create LVSum, a benchmark of long videos from 13 different domains, each accompanied by human-written summaries that include exact timestamps for events. They test several proprietary and open-source MLLMs on this benchmark using both standard metrics and new LLM-based ones that assess content relevance and how well the summary matches the video's timing. The results show consistent shortcomings in how these models handle the sequence and timing of events over long durations. This is important because reliable video summarization requires not just understanding what is in the video but also when things occur, which affects usefulness in real tasks like content creation or analysis. The work provides a foundation for developing better temporal reasoning capabilities in these models.

Core claim

LVSum is a human-annotated benchmark for timestamp-aware long video summarization that reveals systematic gaps in temporal understanding among existing MLLMs when evaluated with LLM-based metrics for content relevance and modality coherence.

What carries the argument

The LVSum benchmark, which pairs diverse long-form videos with human-generated summaries containing precise temporal references, along with newly introduced LLM-based metrics for assessing temporal fidelity.

If this is right

  • MLLMs require improved methods for tracking events across extended video lengths.
  • Evaluations of future models should include checks for both semantic accuracy and temporal alignment.
  • Insights from LVSum can guide the development of models with stronger temporal reasoning.
  • Standard metrics alone are insufficient for capturing timing errors in summaries.

Where Pith is reading between the lines

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

  • Similar benchmarks could be developed for other video tasks like captioning or question answering to test temporal skills more broadly.
  • Improving temporal understanding might also enhance performance on shorter videos or related multimodal tasks.
  • The use of LLM-based metrics suggests a scalable way to evaluate without relying solely on human judges for every test.

Load-bearing premise

Human-generated summaries with precise temporal references combined with LLM-based metrics reliably and unbiasedly measure the temporal fidelity of model outputs.

What would settle it

Re-annotating videos in LVSum with new independent human summaries and finding that model scores on temporal metrics change substantially or reverse the observed gaps.

Figures

Figures reproduced from arXiv: 2604.10024 by Alkesh Patel, Ganesh Nagarajan, Melis Ozyildirim, Ying-Chang Cheng.

Figure 1
Figure 1. Figure 1: Distribution of video categories in the LVSum dataset. select 100 videos using weighted sampling proportional to the observed category distribution. This strategy preserves the natural long-tailed distribution of real￾world video content while avoiding over-representation of dominant categories that would result from uniform sampling. The selected videos are then sent to human annotators for summarization.… view at source ↗
Figure 2
Figure 2. Figure 2: Correlation vs. summary length for different models. Solid lines denote Kendall’s τ , dashed lines denote Spearman’s ρ. on model’s ability to effectively rank summary segments under compression. Crucially, this compression-conditioned analysis is enabled by LVSum’s interval￾level importance annotations with multiple references. Unlike VideoXum and Instruct-V2Xum ( [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Video summarization comparison for a selected video from LVSum. Human Summary S1 (00:03–00:10): score: 3: The title Dulcimeria with Bing Futch is displayed. S2 (00:12–00:16): score: 3: The title, Episode 328 – “One-way Ticket” is displayed. S3 (00:32–00:40): score: 3: The artist starts singing the song “One-Way Ticket” and playing guitar. S4 (04:30–04:36): score: 3: The artist completes singing the song “O… view at source ↗
Figure 4
Figure 4. Figure 4: Failure cases illustrating distinct evaluation modes. (a) Low Content Relevance (CR): summary omits salient events. (b) Low Modality Coherence (MC): textual descriptions contradict visual evidence within the predicted interval. 6 Conclusion In this work we introduced LVSum, a benchmark for timestamp-aware long￾video summarization with multi-reference human annotations and interval-level importance supervis… view at source ↗
Figure 5
Figure 5. Figure 5: Comparison of video summarization results across different models and videos Video 1 Video 2 Human Grading S1 (00:04–00:09): score: 3: Heavy rain lashed Mumbai, caused an overflow of water supplying lake. S2 (00:23–00:27): score: 2: Some schools and colleges have been shut down in several districts. S3 (00:37–00:41): score: 3: Rainfall is over 60 mm in various spots. S4 (00:44–00:49): score: 2: 6 teams has… view at source ↗
read the original abstract

Long video summarization presents significant challenges for current multimodal large language models (MLLMs), particularly in maintaining temporal fidelity over extended durations and producing summaries that are both semantically and temporally grounded. In this work, we present LVSum, a human-annotated benchmark designed specifically for evaluating long video summarization with fine-grained temporal alignment. LVSum comprises diverse long-form videos across 13 domains, each paired with human-generated summaries containing precise temporal references. We conduct a comprehensive evaluation of both proprietary and open-source MLLMs on LVSum, assessing performance using newly introduced LLM-based metrics for content relevance and modality coherence, alongside standard evaluation metrics. Our experiments reveal systematic gaps in temporal understanding among existing MLLMs and offer insights that establish a new foundation for advancing temporal reasoning in long video summarization.

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

3 major / 2 minor

Summary. The paper introduces LVSum, a human-annotated benchmark for timestamp-aware long video summarization comprising diverse long-form videos across 13 domains, each paired with human-generated summaries that include precise temporal references. It evaluates both proprietary and open-source MLLMs using standard metrics together with newly proposed LLM-based metrics for content relevance and modality coherence. The central claim is that existing MLLMs exhibit systematic gaps in temporal understanding, with the benchmark and metrics intended to establish a foundation for advancing temporal reasoning in long video summarization.

Significance. If the evaluation pipeline proves reliable, LVSum could serve as a useful standardized benchmark for assessing temporal fidelity in long-video MLLMs and help surface concrete limitations in current models' handling of extended temporal structure. The construction of human summaries with explicit timestamps across multiple domains is a constructive step toward more grounded evaluation in multimodal video understanding.

major comments (3)
  1. The central claim of systematic gaps in MLLM temporal understanding rests on the newly introduced LLM-based metrics for content relevance and modality coherence. The manuscript does not report any validation of these metrics against human judgments (e.g., Pearson or Spearman correlation with human raters on temporal alignment tasks), leaving open the possibility that the metrics inherit or amplify the same temporal weaknesses they are meant to measure.
  2. Benchmark Construction section: the description of the human annotation process for the 13-domain dataset supplies no inter-annotator agreement statistics, annotation guidelines, or quality-control procedures. Because the ground-truth summaries with precise timestamps are the reference against which all model outputs are scored, the absence of these details directly affects the credibility of the reported performance gaps.
  3. Experiments section: performance differences between models are presented without statistical significance tests (e.g., paired t-tests or bootstrap confidence intervals on the metric scores). Without such tests it is difficult to determine whether the observed gaps are systematic or could arise from sampling variance in the test videos.
minor comments (2)
  1. The abstract and evaluation sections refer to 'newly introduced LLM-based metrics' but do not include the exact prompt templates or few-shot examples used to query the judge LLM; providing these would improve reproducibility.
  2. A summary table listing video durations, domain distribution, and number of annotated summaries per domain would help readers quickly assess the scale and balance of LVSum.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their insightful comments, which will help improve the quality and rigor of our manuscript. We address each major comment in turn below.

read point-by-point responses

  1. Referee: The central claim of systematic gaps in MLLM temporal understanding rests on the newly introduced LLM-based metrics for content relevance and modality coherence. The manuscript does not report any validation of these metrics against human judgments (e.g., Pearson or Spearman correlation with human raters on temporal alignment tasks), leaving open the possibility that the metrics inherit or amplify the same temporal weaknesses they are meant to measure.

    Authors: We agree that validating the LLM-based metrics against human judgments would strengthen the central claims. In the revised manuscript, we will add a human validation study on a sampled subset of LVSum, reporting Pearson and Spearman correlations between the proposed metrics and human ratings specifically on temporal alignment tasks. revision: yes

  2. Referee: Benchmark Construction section: the description of the human annotation process for the 13-domain dataset supplies no inter-annotator agreement statistics, annotation guidelines, or quality-control procedures. Because the ground-truth summaries with precise timestamps are the reference against which all model outputs are scored, the absence of these details directly affects the credibility of the reported performance gaps.

    Authors: We acknowledge that these details are essential for establishing benchmark credibility. The revised Benchmark Construction section will include inter-annotator agreement statistics, the annotation guidelines, and descriptions of the quality-control procedures used during dataset creation. revision: yes

  3. Referee: Experiments section: performance differences between models are presented without statistical significance tests (e.g., paired t-tests or bootstrap confidence intervals on the metric scores). Without such tests it is difficult to determine whether the observed gaps are systematic or could arise from sampling variance in the test videos.

    Authors: We agree that statistical significance testing is required to support claims of systematic gaps. In the revised Experiments section, we will report paired t-tests and bootstrap confidence intervals on the metric scores to quantify the reliability of the observed performance differences. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical benchmark and evaluation

full rationale

The paper constructs a human-annotated benchmark (LVSum) across 13 domains with timestamped summaries and evaluates MLLMs using a mix of standard metrics plus newly introduced LLM-based ones for relevance and coherence. No mathematical derivations, equations, fitted parameters, predictions, or first-principles claims appear. All steps are dataset creation and empirical measurement against external human references; nothing reduces to its own inputs by construction. Self-citations, if present, are not load-bearing for any derivation chain. The work is self-contained as standard benchmark research without circular reasoning.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is an empirical benchmark paper. No free parameters, axioms, or invented entities are invoked or required for the central claim.

pith-pipeline@v0.9.0 · 5444 in / 1052 out tokens · 37891 ms · 2026-05-10T15:47:38.722708+00:00 · methodology

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Reference graph

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    **Key Event Coverage** – Are the important events from GT present?

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