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LVSum: A Benchmark for Timestamp-Aware Long Video Summarization

Alkesh Patel, Melis Ozyildirim, Ying-Chang Cheng, Ganesh Nagarajan

arXiv:2604.10024v2cs.CVcs.AIcs.LG

TL;DR

Long-video summarization remains difficult for MLLMs to ground precisely across extended sequences. LVSum provides a human-annotated, temporally aligned benchmark and introduces metrics for relevance and cross-modal coherence. Evaluations find strong semantic understanding but persistent weaknesses in temporal compression, grounding, and constraint adherence.

  • Problem

    MLLMs still face limits in temporal precision and scalability over long video sequences, while comprehensive evaluation of long-video summaries remains a challenge.

    Source: §1 · p. 1

  • Method

    LVSum benchmarks long-video summarization with human annotations and fine-grained temporal alignment, alongside Content Relevance and Modality Coherence metrics.

    Source: §1 · p. 2

  • Results

    Current systems show strong semantic understanding but lack robust temporal compression, multimodal grounding, and constraint adherence; Gemini-2.5-Pro has the highest Modality Coherence (MC = 4.32).

    Source: §5.1 · p. 8, §6 · p. 14

  • Takeaways & Limitations

    The observed category-dependent behavior and failure modes motivate richer evaluation dimensions such as Content Relevance and Modality Coherence.

    Source: §6 · p. 14

Abstract

from arXiv · show

Long video summarization presents significant challenges for multimodal large language models (MLLMs), particularly in maintaining temporal fidelity over extended durations and producing summaries that are both semantically and temporally grounded. We introduce LVSum, a human-annotated benchmark for evaluating long-form video summarization with fine-grained temporal alignment. LVSum comprises 72 diverse videos spanning 13 domains with an average duration of 16 minutes, each annotated with up to 10 human-generated summaries containing temporal references. We conduct a comprehensive evaluation of leading proprietary and open-source MLLMs using newly introduced LLM-based metrics for content relevance and modality coherence, alongside standard automatic metrics. Our experiments reveal three key findings: (1) transcripts contribute substantially more to summarization quality than visual frames alone, (2) a significant performance gap persists between model-generated and human-written summaries, and (3) current MLLMs exhibit systematic weaknesses in temporal grounding, instruction adherence, and cross-modal coherence. We release the dataset and code.

1 Introduction

Long-video summarization requires temporal precision and scalability that current MLLMs struggle to provide. The paper introduces LVSum, new cross-modal evaluation metrics, and a systematic study of proprietary and open-source models.

1 Introduction

  • Existing MLLMs face gaps in temporal grounding, long-context processing, instruction following, and holistic evaluation for video summarization.The introduction notes that current automatic metrics inadequately capture semantic relevance, temporal coherence, and category-dependent preferences.Source: §1 · pp. 1–2
  • LVSum provides a long-duration benchmark with human-annotated, fine-grained temporal summaries across diverse video domains.It is designed to evaluate coherent, timestamp-anchored summaries from lengthy video inputs.Source: §1 · p. 2
  • A systematic evaluation of proprietary and open-source MLLMs uncovers gaps in tracking narrative flow over time.The study also analyzes failure modes involving temporal grounding, instruction adherence, and cross-modal reasoning under extended contexts.Source: §1 · pp. 2–3
  • The paper introduces Content Relevance and Modality Coherence to assess semantic faithfulness and cross-modal grounding in long-video summaries.These LLM-based metrics address limitations of existing rank-based correlation metrics.Source: §1 · p. 2

2 Related Work

Related work distinguishes video summarization task types and reviews datasets, MLLM methods, and evaluation metrics. It identifies long-form V2VT summarization and temporal–description coherence as gaps addressed by LVSum.

Video summarization tasks

  • Video summarization includes V2V, V2T, Q2V, and V2VT tasks; this paper focuses on V2VT, producing timestamped intervals and descriptions from video and transcript inputs.“Video summarization” refers to V2VT unless otherwise specified.Source: §2 · p. 3

Video Summarization Datasets

  • Prior datasets are described as short-video or structurally limited, leaving a gap for long videos with human annotations and contiguous temporal intervals.VideoXum lacks explicit importance scores, while Instruct-V2Xum has single references and frame-level annotations.Source: §2 · pp. 3–4

Evaluation protocols and metrics

  • Existing cross-modal metrics emphasize semantic relevance or modal similarity, while overlooking coherence between predicted timestamps and generated descriptions.The paper addresses this gap with MLLM-as-judge metrics for content relevance and modality coherence.Source: §2 · p. 4

3 Dataset for Long Video Summarization: LVSum

LVSum is constructed from a diverse pool of long videos and annotated with temporally grounded summaries and interval-level importance scores. Its combination of long-form videos, multiple human references, and interval-level scores distinguishes it from prior datasets.

3.1 Dataset Construction

  • The construction process crawls about 4,000 videos, clusters open-ended labels into 13 categories, and uses weighted sampling to preserve the natural long-tailed distribution.The sampled videos are sent to human annotators for summarization.Source: §3.1 · pp. 4–5
  • Ten independent annotators identify timestamped segments, write descriptions, score relevance, and refine annotations within a 15% duration budget.Submissions are manually reviewed, and annotations violating the instructions or length budget are discarded.Source: §3.1 · p. 5
  • LVSum retains 72 videos after filtering 11 for sensitive content and 17 for source-site usage restrictions.The videos span 10–55 minutes, with an average duration of 16 minutes.Source: §3.1 · p. 5, §3.2 · p. 5

3.2 Comparison with other video summarization dataset

  • LVSum combines long videos, multiple human references, and interval-level importance scores, a combination the paper says no existing dataset offers at scale.The comparison notes that VideoXum lacks explicit importance scores, Instruct-V2Xum lacks contiguous key-shot intervals and multiple references, and EDUVSUM is lecture-specific with one expert annotation.Source: §3.2 · pp. 5–6

4 Experiments and Results

The experiments evaluate proprietary and open-source MLLMs using transcript and frame inputs, rank-based metrics, and LLM-as-judge measures. Baselines provide lower bounds for temporal selection.

4.1 Models Used

  • Sonnet-4.5, Gemini-2.5-Pro, and Qwen3-VL-235B receive 96 uniformly sampled frames and timestamped transcripts as input.The transcript generation uses Gemini-2.5-Pro.Source: §4.1 · p. 6

4.2 Evaluation Metrics

  • Evaluation uses Kendall’s τ and Spearman’s ρ as primary metrics, with average predicted summary length reported to contextualize scores.Importance rankings are computed at second-level granularity, using video samples at 1 fps to match the temporal precision of MLLM timestamps.Source: §4.2 · p. 6
  • Content Relevance scores semantic alignment with reference summaries, while Modality Coherence assesses agreement between interval visuals, audio, and text.Both MLLM-as-judge metrics use a 1–5 scale, with higher scores indicating stronger relevance or cross-modal consistency.Source: §4.2 · pp. 6–7

4.3 Baselines

  • Random and evenly spaced interval baselines use 3–5-second segments, random importance scores, and an approximately 15% video-duration budget.These baselines establish lower bounds for temporal selection and highlight the contribution of visual and multimodal cues in MLLM-based summarization.Source: §4.3 · p. 7

5 Results and Discussion

LVSum results show that models can identify salient content but remain inconsistent in temporal alignment, compression, and cross-modal grounding. Performance also varies with summary length, input modality, frame sampling, and video category.

5.1 Main Results

  • Human agreement reaches τ = 0.13388 and ρ = 0.13635; Opus-4.5 leads models in temporal agreement at τ = 0.10093 and ρ = 0.10513.Opus-4.5 reaches approximately 75% of human-level performance; Gemini-2.5-Pro follows closely, while Qwen3-VL-235B lags.Source: §5.1 · p. 7, Table 2 · p. 8
  • Models show over-coverage, temporal misalignment, and cross-modal mismatch despite high Content Relevance; Gemini-2.5-Pro has the highest Modality Coherence (MC = 4.32).Opus-4.5 and Qwen3-VL-235B exceed the 15% summary budget (30% and 56%), while Gemini-2.5-Pro selects 12%.Source: §5.1 · p. 8

5.2 Length-Constrained Summary Analysis

  • At a 15% budget, Opus-4.5 slightly outperforms Gemini-2.5-Pro, while Qwen3-VL-235B trails; increasing summary length does not uniformly improve performance.Opus improves as the budget rises from 10% to 30%; Gemini is already strong at 10–15%, while Qwen has a nonmonotonic trend.Source: §5.2 · pp. 8–9, Figure 2 · p. 9

Input Modality Ablations

  • Transcript-only input outperforms video-only input for all three models; combining modalities benefits Opus-4.5 and Gemini-2.5-Pro but reduces Qwen3-VL-235B's τ below transcript-only.For Opus-4.5, moving from video-only to transcript-only increases τ by 44%, from 0.06182 to 0.08879; Qwen's combined input coincides with extreme verbosity and instruction drift.Source: Input Modality Ablations · pp. 9–10, Table 3 · p. 10

Video Sampling Density Ablations

Ablations by Categories

  • Category-wise results differ: Gemini-2.5-Pro is strong on Vlogs and News Reports, Opus-4.5 on Lectures, and Qwen3-VL-235B collapses on event-dense domains.Opus systematically exceeds the length budget, while Qwen remains highly verbose; global averages can obscure category-level biases.Source: Ablations by Categories · pp. 11–12, Table 5 · p. 11

Comparison of outputs from various models

  • In qualitative comparisons, Gemini-2.5-Pro produces compact, storyline-aligned summaries, while Opus-4.5 is longer and Qwen3-VL-235B often paraphrases most events.The same behavior appears across a selected video, a monsoon-flooding news report, and a school-trip video to Normandy.Source: Comparison of outputs from various… · p. 12

Comparison of MLLM-as-Judge metrics

  • Content Relevance and Modality Coherence capture distinct failures that τ/ρ can miss: omitted narrative beats and descriptions misaligned with predicted intervals.The examples show that reasonable temporal ranking can coexist with low content relevance or poor modality grounding.Source: Comparison of MLLM-as-Judge metrics · pp. 12, 14, Figure 4 · p. 14

6 Conclusion

LVSum provides a benchmark for timestamp-aware long-video summarization with multi-reference annotations and interval-level importance supervision. Evaluations show that current MLLMs still struggle to balance semantic salience, temporal compression, and multimodal grounding.

6 Conclusion

  • LVSum combines multi-reference human annotations with interval-level importance supervision for timestamp-aware long-video summarization.The authors report that evaluated systems show strong semantic understanding but lack robust temporal compression, multimodal grounding, and constraint adherence.Source: §6 · p. 14

A Annotation Guidelines for LVSum Dataset

The annotation guidance asks annotators to select relevant, accurate, concise, non-redundant, informative segments. Segment selection must prioritize meaningful highlights, avoid low-value or duplicated content, and remain within a 15% duration limit.

A Annotation Guidelines for LVSum Dataset

  • The annotation goal is a short, informative summary that selects, describes, and ranks the video’s most relevant segments.Guidance emphasizes relevance, accuracy, concision, non-redundancy, and informativeness.Source: A Annotation Guidelines for LVSum… · p. 17

Video Segmentation

  • Selected segments should cover meaningful highlights factually, avoid irrelevant or overlapping material, and total no more than 15% of video duration.Each segment should fully cover its target information.Source: Video Segmentation · p. 17

Segment Ranking

Segments are ranked by how essential they are to understanding the video’s main message, with lower-priority material included only if the 15% limit allows.

Segment Ranking

  • The ranking scale distinguishes somewhat relevant segments as less critical and usable only if time remains within the 15% limit.The scale is titled “Ranking Scale for Video Segment Importance.”Source: Segment Ranking · p. 18, Table 6 · p. 18

Review Your Task

The appendix specifies timestamped segment summaries with a 15% duration cap and review checks, then defines prompts for evaluating content relevance and modality coherence.

Review Your Task

  • The final review prioritizes essential segments, checks description accuracy and consistency, and requires total duration ≤15% with no overlaps.Segments are ranked, with the most crucial assigned rank 3.Source: Review Your Task · p. 18

B.1 Transcript-Only Prompt

  • Transcript-only summaries request timestamped segments, 0–3 relevance scores, brief descriptions, and a combined duration no greater than 15% of the video.The prompt supplies the transcript and an example list format.Source: §B.1 · p. 19

B.2 Video-Only and Video+Transcript Prompt

  • Video-only and video-plus-transcript prompts request timestamped, scored segments under the same 15% duration cap, with frames sampled at listed timestamps.The prompt asks for both visual content and the transcript when provided; video-only runs use a placeholder when no transcript is available.Source: §B.2 · p. 20

C.1 Content Relevance Prompt

  • Content Relevance scores how well a generated summary preserves ground-truth events and meaning, penalizing omissions, irrelevant additions, and misrepresentation rather than wording differences.The prompt requests an integer score from 1 to 5 and a short justification.Source: §C.1 · p. 21

C.2 Modality Coherence Prompt

  • Modality Coherence scores whether summary statements are supported by video frames or audio, penalizing hallucinations, contradictions, and cross-modal mismatches.The criteria include visual grounding, audio-visual consistency, hallucination checks, and cross-modal agreement; the prompt requests an integer score from 1 to 5.Source: §C.2 · p. 22

D Constraint Compliance Test for Qwen3VL

The Qwen3-VL-235B constraint test repositions the length instruction in video-plus-transcript prompts to probe possible position forgetting; the appendix also includes ablation tables and qualitative comparisons.

D Constraint Compliance Test for Qwen3VL

  • The test repositions the length constraint first, last, or in a sandwich arrangement to check whether Qwen3-VL-235B forgets it in video-plus-transcript mode.The stated intuition is that better compliance when the constraint appears later would indicate recency bias or position forgetting.Source: D Constraint Compliance Test for Qwen3VL · p. 23

E Additional Qualitative Examples

  • Table 7 presents category ablations for Qwen3-VL-235B with video-plus-transcript input, while Figure 5 compares summarization results across models and videos.The supplied captions do not specify the outcome of either comparison.Source: Table 7 · p. 23, Figure 5 · p. 24

F Additional Ablations on number of input frames for video-only evaluations with Opus-4.5 model

The appendix includes an ablation table on the number of input frames for video-only evaluation with Opus-4.5.

F Additional Ablations on number of input frames for video-only evaluations with Opus-4.5 model

  • Table 8 presents ablations examining the number of input frames used for video-only evaluation, as stated in the caption.Source: Table 8 · p. 25
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