mlsanityData to 7 Oct 2026

Lotus: Creating Short Videos From Long Videos With Abstractive and Extractive Summarization

arXiv 2502.07096 ↗First posted 10 Feb 2025cs.HC cs.CVcited by 6

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Abstract (arXiv)

Short-form videos are popular on platforms like TikTok and Instagram as they quickly capture viewers' attention. Many creators repurpose their long-form videos to produce short-form videos, but creators report that planning, extracting, and arranging clips from long-form videos is challenging. Currently, creators make extractive short-form videos composed of existing long-form video clips or abstractive short-form videos by adding newly recorded narration to visuals. While extractive videos maintain the original connection between audio and visuals, abstractive videos offer flexibility in selecting content to be included in a shorter time. We present Lotus, a system that combines both approaches to balance preserving the original content with flexibility over the content. Lotus first creates an abstractive short-form video by generating both a short-form script and its corresponding speech, then matching long-form video clips to the generated narration. Creators can then add extractive clips with an automated method or Lotus's editing interface. Lotus's interface can be used to further refine the short-form video. We compare short-form videos generated by Lotus with those using an extractive baseline method. In our user study, we compare creating short-form videos using Lotus to participants' existing practice.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 23 of its 55 references that are in the map. First posted on arXiv 10 Feb 2025.

Builds on

23 of 55 references.

References · 23

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2024VoiceCraft: Zero-Shot Speech Editing and Text-to-Speech in the Wildcited by 118
  4. 2023Powerset multi-class cross entropy loss for neural speaker diarizationcited by 109
  5. 2019Text-based Editing of Talking-head Videocited by 102
  6. 2017Video Summarization with Attention-Based Encoder-Decoder Networkscited by 63
  7. 2017Text Summarization Techniques: A Brief Surveycited by 59
  8. 2023TextrolSpeech: A Text Style Control Speech Corpus With Codec Language Text-to-Speech Modelscited by 48
  9. 2024LAVE: LLM-Powered Agent Assistance and Language Augmentation for Video Editingcited by 47
  10. 2019Hierarchical Recurrent Neural Network for Video Summarizationcited by 44
  11. 2019Multimodal Abstractive Summarization for How2 Videoscited by 41
  12. 2023TorchAudio 2.1: Advancing speech recognition, self-supervised learning, and audio processing components for PyTorchcited by 25
11 more
  1. 2020Rescribe: Authoring and Automatically Editing Audio Descriptionscited by 20
  2. 2024SpeechCraft: A Fine-grained Expressive Speech Dataset with Natural Language Descriptioncited by 20
  3. 2023AVscript: Accessible Video Editing with Audio-Visual Scriptscited by 19
  4. 2023ReelFramer: Human-AI Co-Creation for News-to-Video Translationcited by 19
  5. 2019B-Script: Transcript-based B-roll Video Editing with Recommendationscited by 16
  6. 2024ExpressEdit: Video Editing with Natural Language and Sketchingcited by 14
  7. 2024EmoKnob: Enhance Voice Cloning with Fine-Grained Emotion Controlcited by 13
  8. 2023PodReels: Human-AI Co-Creation of Video Podcast Teaserscited by 12
  9. 2021Hierarchical Summarization for Longform Spoken Dialogcited by 9
  10. 2022Video Summarization Overviewcited by 6
  11. 2017ElasticPlay: Interactive Video Summarization with Dynamic Time Budgetscited by 4

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Citing papers per year: 2025: 1; 2026: 5.

6 papers.