MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing | Paper Citation Record

Observation ecced3ec-8956-43b8-8873-8915804c5eca · outbound

This paper cites Adopting self- supervised learning into unsupervised video summarization through restorative score.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Adopting self- supervised learning into unsupervised video summarization through restorative score

Reference 1

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Observation acda662c-6162-4503-9b3c-fd3458d436e4 · outbound

This paper cites Combining global and local attention with positional encoding for video summarization.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Combining global and local attention with positional encoding for video summarization

Reference 2

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Observation 27d5d21a-0523-4123-a40c-ae1ba4b9053d · outbound

This paper cites Summarizing videos using con- centrated attention and considering the uniqueness and diver- sity of the video frames.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Summarizing videos using con- centrated attention and considering the uniqueness and diver- sity of the video frames

Reference 3

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Observation 59baaf17-8b2b-4b27-b110-c428ee0f6d7c · outbound

This paper cites Scaling up video summarization pretraining with large language models.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Scaling up video summarization pretraining with large language models

Reference 4

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Observation 1de320e2-cd3a-4139-94e1-2109e9b99123 · outbound

This paper cites Qwen3-VL Technical Report.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Qwen3-VL Technical Report

Reference 5

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This paper cites Blender studio films.https://studio.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Blender studio films.https://studio

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Observation 093d8ffd-64fb-4612-9cf7-98c1eedbb5ec · outbound

This paper cites VSUMM: A mechanism designed to produce static video summaries and a novel evaluation method.Pattern Recog- nit.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing VSUMM: A mechanism designed to produce static video summaries and a novel evaluation method.Pattern Recog- nit

Reference 7

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Observation 87394ed8-fd48-4735-977e-5a16af907239 · outbound

This paper cites Summarizing videos with attention.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Summarizing videos with attention

Reference 8

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Observation f517d32b-6f65-4f09-81b4-b8854a870cae · outbound

This paper cites Video-R1: Reinforcing video reasoning in MLLMs.NeurIPS, 38:99114–99137,.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Video-R1: Reinforcing video reasoning in MLLMs.NeurIPS, 38:99114–99137,

Reference 9

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Observation 1526b89c-f3bf-481d-a0f6-4b3f32a69e5b · outbound

This paper cites Au- tomatic non-linear video editing transfer.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Au- tomatic non-linear video editing transfer

Reference 10

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Observation 4612b575-b863-4be5-8268-5df9bfe7066b · outbound

This paper cites Video-MME: The first-ever comprehensive evaluation benchmark of multi- modal LLMs in video analysis.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Video-MME: The first-ever comprehensive evaluation benchmark of multi- modal LLMs in video analysis

Reference 11

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Observation 1049e734-6252-40a7-b747-b7c66e8b30f5 · outbound

This paper cites Training-free language-guided video summarization via multi-grained saliency scoring.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Training-free language-guided video summarization via multi-grained saliency scoring

Reference 12

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Observation 9aa6d92c-f5ab-46ce-84bf-d1d51241a196 · outbound

This paper cites Supervised video summarization via multiple feature sets with parallel attention.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Supervised video summarization via multiple feature sets with parallel attention

Reference 13

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Observation 5a58f19a-82d1-447d-a574-17ed5d27a40b · outbound

This paper cites A Survey on LLM-as-a-Judge.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing A Survey on LLM-as-a-Judge

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Observation c728aa61-d9b1-4197-b6a2-5da281883f28 · outbound

This paper cites VTG-LLM: Integrating timestamp knowledge into video LLMs for enhanced video temporal grounding.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing VTG-LLM: Integrating timestamp knowledge into video LLMs for enhanced video temporal grounding

Reference 15

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Observation 3ca9901d-096e-4061-ab4c-97a148843892 · outbound

This paper cites Creating summaries from user videos.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Creating summaries from user videos

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Observation a7d90a2c-4405-41d9-aae1-0bf39a09d96b · outbound

This paper cites Align and attend: Multimodal summarization with dual contrastive losses.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Align and attend: Multimodal summarization with dual contrastive losses

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Observation acfa2fc3-39d0-46a4-9ed5-1b8035c2a4de · outbound

This paper cites MovieNet: A holistic dataset for movie under- standing.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing MovieNet: A holistic dataset for movie under- standing

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Observation 6fd57df8-6057-4080-ac17-65e9d06b8542 · outbound

This paper cites Joint video summarization and moment localization by cross-task sample transfer.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Joint video summarization and moment localization by cross-task sample transfer

Reference 19

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Observation a192e377-fd68-4f53-8236-3dcd1e8de382 · outbound

This paper cites Discriminative feature learning for unsu- pervised video summarization.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Discriminative feature learning for unsu- pervised video summarization

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Observation e914b6eb-0492-4220-8877-28d176408329 · outbound

This paper cites Dense-captioning events in videos.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Dense-captioning events in videos

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Observation 2da01de5-bbbf-4d77-8beb-e4ccfb1c4176 · outbound

This paper cites Computational video editing for dialogue-driven scenes.ACM Trans.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Computational video editing for dialogue-driven scenes.ACM Trans

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Observation f3f5c708-8ebf-4238-8d97-484837367b00 · outbound

This paper cites Video sum- marization with large language models.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Video sum- marization with large language models

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Observation 86900be1-3d86-456f-a173-501ca56e32a4 · outbound

This paper cites Detecting mo- ments and highlights in videos via natural language queries.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Detecting mo- ments and highlights in videos via natural language queries

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Observation b63513ae-f924-4abf-ad7b-743101a434e0 · outbound

This paper cites Progressive video summarization via multimodal self- supervised learning.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Progressive video summarization via multimodal self- supervised learning

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Observation 062e7a7a-18d2-4467-adf9-01a0a304ad21 · outbound

This paper cites Progressive video summarization via multimodal self- supervised learning.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Progressive video summarization via multimodal self- supervised learning

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Observation da640c86-dd3c-4611-b5c5-b1c815b40c3a · outbound

This paper cites VideoChat-R1: Enhancing spatio-temporal percep- tion via reinforcement fine-tuning.NeurIPS, 2025.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing VideoChat-R1: Enhancing spatio-temporal percep- tion via reinforcement fine-tuning.NeurIPS, 2025

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Observation 41c3e89d-2252-4411-b6d1-10fcd170bf27 · outbound

This paper cites VideoXum: cross- modal visual and textural summarization of videos.IEEE Trans.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing VideoXum: cross- modal visual and textural summarization of videos.IEEE Trans

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Observation b70bf6a0-9822-46b6-8405-91157efb81a6 · outbound

This paper cites Visual instruction tuning.NeurIPS, 36:34892–34916, 2023.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Visual instruction tuning.NeurIPS, 36:34892–34916, 2023

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Observation 4fcf045e-d7f8-4251-8ddc-1a187e0b7952 · outbound

This paper cites G-Eval: NLG evaluation using gpt-4 with better human alignment.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing G-Eval: NLG evaluation using gpt-4 with better human alignment

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Observation 49c0c03c-75b9-41ff-81c8-1c126a6ae26b · outbound

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MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Unresolved cited work

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Observation 1ebf3e82-6a7c-431d-8d72-8db30a0f133b · outbound

This paper cites Chrono: A simple blueprint for representing time in MLLMs.arXiv preprint arXiv:2406.18113, 2024.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Chrono: A simple blueprint for representing time in MLLMs.arXiv preprint arXiv:2406.18113, 2024

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Observation ec5547e9-99dc-4c2c-b7f1-3d9f24c42fdf · outbound

This paper cites CLIP-It! language-guided video summarization.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing CLIP-It! language-guided video summarization

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This paper cites Rethinking the evaluation of video summaries.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Rethinking the evaluation of video summaries

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This paper cites Contrastive losses are natural criteria for unsu- pervised video summarization.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Contrastive losses are natural criteria for unsu- pervised video summarization

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This paper cites Mea- sure Twice, Cut Once: A semantic-oriented approach to video temporal localization with video LLMs.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Mea- sure Twice, Cut Once: A semantic-oriented approach to video temporal localization with video LLMs

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This paper cites MovieCuts: A new dataset and benchmark for cut type recognition.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing MovieCuts: A new dataset and benchmark for cut type recognition

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MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Generative timelines for instructed visual assembly

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This paper cites MMSum: A dataset for multimodal summarization and thumbnail gen- eration of videos.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing MMSum: A dataset for multimodal summarization and thumbnail gen- eration of videos

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This paper cites TimeChat: A time-sensitive multimodal large language model for long video understanding.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing TimeChat: A time-sensitive multimodal large language model for long video understanding

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MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Unresolved cited work

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This paper cites Judging the judges: A system- atic study of position bias in LLM-as-a-Judge.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Judging the judges: A system- atic study of position bias in LLM-as-a-Judge

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This paper cites Generic event boundary de- tection: A benchmark for event segmentation.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Generic event boundary de- tection: A benchmark for event segmentation

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This paper cites CSTA: Cnn- based spatiotemporal attention for video summarization.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing CSTA: Cnn- based spatiotemporal attention for video summarization

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MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Csta: Cnn- based spatiotemporal attention for video summarization

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Observation e7cc1cd1-8c6a-4491-aa08-5d0f4bdeeb89 · outbound

This paper cites TVSum: Summarizing web videos using titles.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing TVSum: Summarizing web videos using titles

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Observation 4094f2cd-4abe-4731-b9d8-8afde550e466 · outbound

This paper cites Language-guided self-supervised video summarization using text semantic matching considering the diversity of the video.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Language-guided self-supervised video summarization using text semantic matching considering the diversity of the video

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Observation 04c50e48-d688-4b89-b878-b6073eca71b3 · outbound

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MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Gemini: A family of highly capable multimodal models, 2025

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MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing QuickCut: An interactive tool for editing narrated video

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MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Query Twice: Dual mixture attention meta learning for video summarization

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This paper cites Write-A-Video: Computational video montage from themed text.ACM Trans.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Write-A-Video: Computational video montage from themed text.ACM Trans

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This paper cites InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency

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Observation a1397759-3ae4-4575-a7bc-fe20cf4c8182 · outbound

This paper cites Time-R1: Post-training large vision language model for temporal video grounding.NeurIPS, 38:83330– 83364, 2026.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Time-R1: Post-training large vision language model for temporal video grounding.NeurIPS, 38:83330– 83364, 2026

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This paper cites LongVideoBench: a benchmark for long-context interleaved video-language understanding.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing LongVideoBench: a benchmark for long-context interleaved video-language understanding

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MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Transcript to Video: Efficient clip sequencing from texts

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Observation 000544e0-d069-4b55-a5de-b07b9c020bcb · outbound

This paper cites Vid2seq: Large-scale pretraining of a vi- sual language model for dense video captioning.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Vid2seq: Large-scale pretraining of a vi- sual language model for dense video captioning

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This paper cites TimeLens: Rethinking video temporal grounding with multimodal LLMs.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing TimeLens: Rethinking video temporal grounding with multimodal LLMs

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Observation 355f80e7-92a6-489f-bca3-d1ee81bf4c9d · outbound

This paper cites GPT-4V(ision) as a Generalist Evaluator for Vision-Language Tasks.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing GPT-4V(ision) as a Generalist Evaluator for Vision-Language Tasks

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Observation 6d41d762-6f0e-471f-bb80-5be68a0be5f8 · outbound

This paper cites Re- constructive sequence-graph network for video summariza- tion.IEEE Trans.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Re- constructive sequence-graph network for video summariza- tion.IEEE Trans

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This paper cites Xing, Hao Zhang, Joseph E.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Xing, Hao Zhang, Joseph E

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Observation 38eaef64-af30-48cd-ab3a-81913c92698b · outbound

This paper cites Deep semantic and attentive network for unsupervised video summarization.ACM Trans.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Deep semantic and attentive network for unsupervised video summarization.ACM Trans

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Resolution: unresolved; no resolver link, observed 2026-08-01T02:57:35.397619Z

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This paper cites MLVU: Benchmarking multi-task long video understanding.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing MLVU: Benchmarking multi-task long video understanding

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This paper cites Deep reinforce- ment learning for unsupervised video summarization with diversity-representativeness reward.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Deep reinforce- ment learning for unsupervised video summarization with diversity-representativeness reward

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This paper cites Edits” counts edits with at least one temporal reversal; “Cuts.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Edits” counts edits with at least one temporal reversal; “Cuts

Reference 64

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MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing Unresolved cited work

Reference 65

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This paper cites 01:30:50.

MEDit-Bench: A Dataset for Evaluating Message-Driven Narrative Video Editing 01:30:50

Reference 66

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