29 citations · 49 across the 10 of their papers we have counts for
10 papers
Speak for Me: Giving LLMs the Situational Awareness to Participate in a Meeting
Muneeb Khan, Frederic Kirstein, Terry Ruas +1
In online meeting delegation, LLM agents fail to recognize when to speak. With no structured way to track stances, coverage, and floor, they miss the moments where they should cont…
Re-FRAME the Meeting Summarization SCOPE: Fact-Based Summarization and Personalization via Questions
Frederic Kirstein, Sonu Kumar, Terry Ruas +1
Meeting summarization with large language models (LLMs) remains error-prone, often producing outputs with hallucinations, omissions, and irrelevancies. We present FRAME, a modular…
You need to MIMIC to get FAME: Solving Meeting Transcript Scarcity with a Multi-Agent Conversations
Frederic Kirstein, Muneeb Khan, Jan Philip Wahle +2
Meeting summarization suffers from limited high-quality data, mainly due to privacy restrictions and expensive collection processes. We address this gap with FAME, a dataset of 500…
Is my Meeting Summary Good? Estimating Quality with a Multi-LLM Evaluator
Frederic Kirstein, Terry Ruas, Bela Gipp
The quality of meeting summaries generated by natural language generation (NLG) systems is hard to measure automatically. Established metrics such as ROUGE and BERTScore have a rel…
Tell me what I need to know: Exploring LLM-based (Personalized) Abstractive Multi-Source Meeting Summarization
Frederic Kirstein, Terry Ruas, Robert Kratel +1
Meeting summarization is crucial in digital communication, but existing solutions struggle with salience identification to generate personalized, workable summaries, and context un…
What's Wrong? Refining Meeting Summaries with LLM Feedback
Frederic Kirstein, Terry Ruas, Bela Gipp
Meeting summarization has become a critical task since digital encounters have become a common practice. Large language models (LLMs) show great potential in summarization, offerin…
CADS: A Systematic Literature Review on the Challenges of Abstractive Dialogue Summarization
Frederic Kirstein, Jan Philip Wahle, Bela Gipp +1
Abstractive dialogue summarization is the task of distilling conversations into informative and concise summaries. Although reviews have been conducted on this topic, there is a la…
What's under the hood: Investigating Automatic Metrics on Meeting Summarization
Frederic Kirstein, Jan Philip Wahle, Terry Ruas +1
Meeting summarization has become a critical task considering the increase in online interactions. While new techniques are introduced regularly, their evaluation uses metrics not d…
Analyzing Multi-Task Learning for Abstractive Text Summarization
Frederic Kirstein, Jan Philip Wahle, Terry Ruas +1
Despite the recent success of multi-task learning and pre-finetuning for natural language understanding, few works have studied the effects of task families on abstractive text sum…
How Large Language Models are Transforming Machine-Paraphrased Plagiarism
Jan Philip Wahle, Terry Ruas, Frederic Kirstein +1
The recent success of large language models for text generation poses a severe threat to academic integrity, as plagiarists can generate realistic paraphrases indistinguishable fro…