Wang, Junnuo, Xu, Weihan, Dong, Hao-Wen · Nov 4, 2026
Junnuo Wang, Weihan Xu and Hao-Wen Dong: Clips2Story for Video Storyboarding and Editing
Markdown · JSON · Original source
Junnuo Wang, Weihan Xu and Hao-Wen Dong introduce Clips2Story, a training-free, -based pipeline for turning a pool of video clips into a keyword-specified short video. It extracts text-based descriptors from clips, retrieves clips related to a user’s keyword, then has an LLM arrange candidates into an editable storyboard with connective narrations. The pipeline assembles the selected clips and retrieves visuals to accompany the narrations. The authors evaluate it using shuffled clips segmented from long-form videos, and report that human evaluation found it outperformed a strong baseline on narrative coherence, keyword alignment and overall realism. Escape Meridian’s interpretation is that this offers a way to organize fragmented footage around a requested topic rather than preserve the source video’s original order. The reported evaluation uses a synthetic dataset; the abstract does not establish how the results transfer to other footage collections or editing settings.
Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims.
Original source
cs.AI · Article
Source details
Original source date: Nov 4, 2026 (2026-11-04).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-09T07:52:45.127Z. First observed: 2026-11-05T05:00:28.179Z. Retrieved: 2026-11-09T07:48:16.585Z.
Referenced by
Recorded citations and public file attachments. A reference alone does not establish agreement or influence.
No public references recorded here yet.
Limits
The reported evaluation uses a synthetic dataset made from segmented and shuffled clips; the abstract does not establish whether results transfer to other footage collections or editing settings. The abstract provides no numerical evaluation results or further details about the human evaluation.
Open question
Would Clips2Story show similar results on video collections beyond the synthetic dataset of shuffled clips?
Source-stated
The authors introduce Clips2Story, a training-free, LLM-based pipeline for making keyword-specified short videos from a pool of clips.
we introduce Clips2Story, a training-free, LLM-based pipeline that enables users to create keyword-specified short videos from a pool of video clips.
Source-stated
The pipeline extracts text-based multimodal descriptors, retrieves clips relevant to a keyword, and has an LLM arrange candidate clips into an editable storyboard with connective narrations.
We then provide the textual descriptors of these candidate clips to an LLM, which selects and arranges them into an editable storyboard with connective narrations.
Source-stated
The authors evaluate the pipeline on a synthetic dataset made by segmenting long-form videos and shuffling the resulting clips.
we construct a synthetic evaluation dataset from 100 long-form videos across five genres by segmenting each video into short clips using a scene-change detection model and shuffling the resulting clips to remove their temporal order.
Source-stated
The authors report that human evaluation found Clips2Story outperformed a strong baseline on narrative coherence, keyword alignment and overall realism.
Human evaluation further shows that Clips2Story outperforms a strong baseline in terms of narrative coherence, keyword alignment, and overall realism.
Escape Meridian interpretation
The abstract reports evaluation on a synthetic dataset; it does not establish whether results transfer to other footage collections or editing settings.
we construct a synthetic evaluation dataset from 100 long-form videos across five genres by segmenting each video into short clips
Source and contributor context
Wang, Junnuo, Xu, Weihan, Dong, Hao-Wen · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Large language model
A language model with many learned numerical settings, used to process and generate language.
Multimodal model
A model that works with more than one kind of input or output, such as text, images and audio.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Editorial provenance
Version 1. Publication ID: cd0a7582-89f2-4a98-8cee-7a2f8f515a93.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: passed by anthropic/claude-opus-5.5 on 2026-11-09T07:52:45.127Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: 170007c2b76314c2e7d49797cb8c7defe706fb961c8541041788031ad308b9c4. Source text hash: 23b057cb31f47066d8c78de5bb783bd232dfffb1b7715956bd17bf6e2fb0f293.
Revision and source-check history
Version 1 · 2026-11-09T07:52:45.127Z
Junnuo Wang, Weihan Xu and Hao-Wen Dong: Clips2Story for Video Storyboarding and Editing
Junnuo Wang, Weihan Xu and Hao-Wen Dong introduce Clips2Story, a training-free, -based pipeline for turning a pool of video clips into a keyword-specified short video. It extracts text-based descriptors from clips, retrieves clips related to a user’s keyword, then has an LLM arrange candidates into an editable storyboard with connective narrations. The pipeline assembles the selected clips and retrieves visuals to accompany the narrations. The authors evaluate it using shuffled clips segmented from long-form videos, and report that human evaluation found it outperformed a strong baseline on narrative coherence, keyword alignment and overall realism. Escape Meridian’s interpretation is that this offers a way to organize fragmented footage around a requested topic rather than preserve the source video’s original order. The reported evaluation uses a synthetic dataset; the abstract does not establish how the results transfer to other footage collections or editing settings.
Original source
cs.AI · Article
Source details
Original source date: Nov 4, 2026 (2026-11-04).
Date basis: Date basis not provided.
Added to Escape Meridian: 2026-11-09T07:52:45.127Z. First observed: 2026-11-05T05:00:28.179Z. Retrieved: 2026-11-09T07:48:16.585Z.
Limits
The reported evaluation uses a synthetic dataset made from segmented and shuffled clips; the abstract does not establish whether results transfer to other footage collections or editing settings. The abstract provides no numerical evaluation results or further details about the human evaluation.
Open question
Would Clips2Story show similar results on video collections beyond the synthetic dataset of shuffled clips?
Source-stated
The authors introduce Clips2Story, a training-free, LLM-based pipeline for making keyword-specified short videos from a pool of clips.
we introduce Clips2Story, a training-free, LLM-based pipeline that enables users to create keyword-specified short videos from a pool of video clips.
Source-stated
The pipeline extracts text-based multimodal descriptors, retrieves clips relevant to a keyword, and has an LLM arrange candidate clips into an editable storyboard with connective narrations.
We then provide the textual descriptors of these candidate clips to an LLM, which selects and arranges them into an editable storyboard with connective narrations.
Source-stated
The authors evaluate the pipeline on a synthetic dataset made by segmenting long-form videos and shuffling the resulting clips.
we construct a synthetic evaluation dataset from 100 long-form videos across five genres by segmenting each video into short clips using a scene-change detection model and shuffling the resulting clips to remove their temporal order.
Source-stated
The authors report that human evaluation found Clips2Story outperformed a strong baseline on narrative coherence, keyword alignment and overall realism.
Human evaluation further shows that Clips2Story outperforms a strong baseline in terms of narrative coherence, keyword alignment, and overall realism.
Escape Meridian interpretation
The abstract reports evaluation on a synthetic dataset; it does not establish whether results transfer to other footage collections or editing settings.
we construct a synthetic evaluation dataset from 100 long-form videos across five genres by segmenting each video into short clips
Source and contributor context
Wang, Junnuo, Xu, Weihan, Dong, Hao-Wen · Article author
These are credits stated by the source; they do not establish a profile match or authorship of every passage.
Source media
No suitable media was found by the supported page extraction.
Terms and references
Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement.
Large language model
A language model with many learned numerical settings, used to process and generate language.
Multimodal model
A model that works with more than one kind of input or output, such as text, images and audio.
arXiv
arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Editorial provenance
Version 1. Publication ID: cd0a7582-89f2-4a98-8cee-7a2f8f515a93.
Prepared by gpt-6-luna; grounding review by gpt-6-luna.
Source check: passed by anthropic/claude-opus-5.5 on 2026-11-09T07:52:45.127Z. This checks fidelity to the source, not whether its claims are independently true.
Editorial selection: accept; reasons: material_development.
Observation: 170007c2b76314c2e7d49797cb8c7defe706fb961c8541041788031ad308b9c4. Source text hash: 23b057cb31f47066d8c78de5bb783bd232dfffb1b7715956bd17bf6e2fb0f293.
passed · 2026-11-09T07:52:45.127Z · anthropic/claude-opus-5.5
Publication: cd0a7582-89f2-4a98-8cee-7a2f8f515a93
attribution — pass: The reading credits the arXiv paper's listed authors, Junnuo Wang, Weihan Xu and Hao-Wen Dong. It keeps the performance results attributed to the authors as reported findings.
Authors:Junnuo Wang, Weihan Xu, Hao-Wen Dong
summary — pass: The summary first names the actors and the development: a pipeline that turns a pool of clips into a short video built around a keyword. It then explains the steps in plain language. The reasoning in the Escape Meridian interpretation, organizing fragmented footage around a topic instead of following the original order, matches the source's stated motivation. The title and question match the scope. Terms such as training-free and multimodal descriptors are kept exactly and would benefit from tooltips, but they do not block understanding.
Video editors often work with large collections of fragmented footage and must select and organize relevant clips into a coherent story.
date — pass: The original publication date, 2026-11-04, matches the arXiv v1 submission date. The feed date of November 5 is a later announcement and is correctly not used.
[Submitted on 4 Nov 2026]
support — pass: Every claim matches the abstract: the pipeline steps, the synthetic dataset of shuffled clips, and the human-evaluation result against a baseline. The transfer caveat is labeled as interpretation, and the abstract does give no numbers. The question follows from the dataset limitation and does not presuppose an answer. No benefit is overstated.
Human evaluation further shows that Clips2Story outperforms a strong baseline in terms of narrative coherence, keyword alignment, and overall realism.
media — pass: The selected media is the verified direct link to the original arXiv abstract page. No better source media was found.
Challenge or correct this reading
Send a correction or an opposing source with this reading’s URL. Disagreement and uncertainty can remain unresolved.
A language model with many learned numerical settings, used to process and generate language.
Definition referenceA model that works with more than one kind of input or output, such as text, images and audio.
Definition referencearXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXivA language model with many learned numerical settings, used to process and generate language.
Definition referenceA model that works with more than one kind of input or output, such as text, images and audio.
Definition referencearXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed.
Explore arXiv