RankCut: A Ranking-Based LLM Approach to Extractive Summarization for Transcript-Based Video Editing

Video recordings of interviews, lectures, and meetings contain valuable moments surrounded by less essential talk. Making a shareable and meaningful shorter version of this content requires significant effort because it combines tedious, repeated operations with personal editorial decisions, which require human judgment. We introduce an editing approach that operates on video transcripts and combines a three-stage large language model pipeline with a timeline-anchored, marker-based interface so editors can inspect and refine suggestions before final assembly. The pipeline first produces an overview summary to maximize content coverage, then induces plain-language selection rules that encode editorial intent, and finally applies rule-conditioned ranking on small transcript windows to mitigate long-context limits, yielding strictly extractive, time-aligned spans under duration constraints. The interface displays groupings of short excerpts using markers with priorities and confidence cues, converting opaque model output into verifiable units within standard video editing workflows. On MeetingBank and MeetingBank-QA datasets, our method outperforms practical extractive baselines at matched lengths. In a within-subjects study with experienced video editors familiar with Premiere Pro video editing software, we found that our marker-based interface provided editors higher efficiency, control, and satisfaction than both a manual editing baseline and an opaque auto-cut condition.

@Article{SLCWBW26,
 	 author =  {Shah, Sana and Leake, Mackenzie and Chu, Kun and Weber, Cornelius and Becherer, Nico and Wermter, Stefan},
 	 title = {RankCut: A Ranking-Based LLM Approach to Extractive Summarization for Transcript-Based Video Editing},
 	 booktitle = {}
 	 journal = {IUI '26: Proceedings of the 31st International Conference on Intelligent User Interfaces},
 	 editors = {}
 	 number = {}
 	 volume = {}
 	 pages = {1476 - 1495},
 	 year = {2026},
 	 month = {Mar},
 	 publisher = {}
 	 doi = {10.1145/3742413.3789115},
 }