HowToNarrate: A General-Domain Benchmark for Synchronized Video Narration with External Knowledge (2026.acl-long)
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| Challenge: | Existing MLLMs overemphasize knowledge retrieval while neglecting prior context, causing redundancy and incoherence. |
| Approach: | They propose a framework that combines context compression, knowledge retrieval, and narration generation to improve models' performance. |
| Outcome: | The proposed method significantly improves MLLM performance over existing models. |
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| Challenge: | Existing studies on dense video captioning and video story generation have made some progress, but in practical applications, we typically require synchronized narrations for ongoing visual scenes. |
| Approach: | They propose a task of Synchronized Video Storytelling to generate synchronized narrations for videos using a benchmark dataset with rich annotations. |
| Outcome: | The proposed framework can generate narrations with the guidance of the generated or predefined storyline and human evaluations validate the effectiveness. |
Movie101v2: Improved Movie Narration Benchmark (2025.acl-long)
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| Challenge: | Automatic movie narration aims to generate video-aligned plot descriptions to assist visually impaired audiences. |
| Approach: | They propose to break down the ultimate goal of automatic movie narration into three stages . they propose a large-scale, bilingual dataset with enhanced data quality . |
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MovieUN: A Dataset for Movie Understanding and Narrating (2022.findings-emnlp)
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| Challenge: | Automatic movie narration generation and narration grounding are important to provide a true movie experience for the blind and visually impaired. |
| Approach: | They propose to use movie clips as a benchmark to support automatic movie narration generation and narration grounding tasks. |
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PedagogyBench: A Cognitive-Driven Benchmark for Multimodal Instructional Video Understanding (2026.findings-acl)
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| Challenge: | Existing video understanding benchmarks do not adequately capture the pedagogical logic embedded in instructional videos. |
| Approach: | They propose a pedagogy-driven segmentation strategy and a dual-stream semantic injection pipeline that combines machine pre-annotation with expert refinement. |
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ProLongVid: A Simple but Strong Baseline for Long-context Video Instruction Tuning (2025.emnlp-main)
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Rui Wang, Bohao Li, Xiyang Dai, Jianwei Yang, Yi-Ling Chen, Zhen Xing, Yifan Yang, Dongdong Chen, Xipeng Qiu, Zuxuan Wu, Yu-Gang Jiang
| Challenge: | Existing approaches to adapt image-focused models for video understanding have not been successful in analyzing long video sequences. |
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AlignMMBench: Evaluating Chinese Multimodal Alignment in Large Vision-Language Models (2025.acl-long)
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| Challenge: | Existing benchmarks focus on basic abilities using nonverbal methods, such as yes-no and multiple-choice questions. |
| Approach: | They propose a benchmark that provides more nuanced evaluations of alignment capabilities for large Vision-Language Models (VLMs) they use a rule-calibrated evaluator that exceeds GPT-4's evaluation ability and a “alignment score” to assess the robustness and stability of models across diverse prompts. |
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Movie101: A New Movie Understanding Benchmark (2023.acl-long)
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| Challenge: | Existing methods to narrate movies with no actors are difficult to implement in real situations . a new metric is proposed to provide the best correlation with human evaluation . |
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NarraBench: A Comprehensive Framework for Narrative Benchmarking (2026.eacl-long)
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| Challenge: | Existing benchmarks for narrative understanding are poorly aligned with existing metrics. |
| Approach: | They propose to use NarraBench to assess aspects of narrative understanding that are either overlooked in current work or are poorly aligned with existing metrics. |
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Multilingual Synopses of Movie Narratives: A Dataset for Vision-Language Story Understanding (2024.findings-emnlp)
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| Challenge: | Story video-text alignment is a core task in computational story understanding, but its progress has been held back by the scarcity of manually annotated video- text correspondences and the heavy concentration on English narrations of Hollywood movies. |
| Approach: | They construct a multilingual video story dataset with 13,166 movie summary videos from 7 languages and manual annotations of fine-grained video-text correspondences. |
| Outcome: | The proposed approach outperforms the SOTA methods on clip accuracy and Sentence IoU scores. |
LongTableBench: Benchmarking Long-Context Table Reasoning across Real-World Formats and Domains (2025.findings-emnlp)
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Liyao Li, Jiaming Tian, Hao Chen, Wentao Ye, Chao Ye, Haobo Wang, Ningtao Wang, Xing Fu, Gang Chen, Junbo Zhao
| Challenge: | Evaluating 52 LLMs reveals that only the strongest models maintain robust performance under increasing context lengths and format diversity. |
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