Two New Papers Published!

30 March 2026

Photo: UHH Knowledge Technology

We have a two new papers published! Here's brief information about them:

RankCut: A Ranking-Based LLM Approach to Extractive Summarization for Transcript-Based Video Editing is published at IUI '26: Proceedings of the 31st International Conference on Intelligent User Interfaces. Above image shows the RankCut pipeline.

Authors: Sana Shah, Mackenzie Leake, Kun Chu, Cornelius Weber, Nico Becherer, Stefan Wermter

Abstract: 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. 

A Parameter-free Adaptive Resonance Theory-based Topological Clustering Algorithm Capable of Continual Learning is published at Neural Computing and Applications.

Authors: Naoki Masuyama, Takanori Takebayashi, Yusuke Nojima, Chu Kiong Loo, Hisao Ishibuchi, Stefan Wermter

Abstract: In general, a similarity threshold (i.e., a vigilance parameter) for a node learning process in Adaptive Resonance Theory (ART)-based algorithms has a significant impact on clustering performance. In addition, an edge deletion threshold in a topological clustering algorithm plays an important role in adaptively generating well-separated clusters during a self-organizing process. In this paper, we propose an ART-based topological clustering algorithm that integrates parameter estimation methods for both the similarity threshold and the edge deletion threshold. The similarity threshold is estimated using a determinantal point process-based criterion, while the edge deletion threshold is defined based on the age of edges. Experimental results with synthetic and real-world datasets show that the proposed algorithm has superior clustering performance to state-of-the-art clustering algorithms without requiring parameter specifications specific to the datasets. Source code is available at this https URL: https://github.com/Masuyama-lab/CAE