13 citations · 65 across the 39 of their papers we have counts for
10 papers · 2 filters
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…
The Promises and Pitfalls of LLM Annotations in Dataset Labeling: a Case Study on Media Bias Detection
Tomas Horych, Christoph Mandl, Terry Ruas +4
High annotation costs from hiring or crowdsourcing complicate the creation of large, high-quality datasets needed for training reliable text classifiers. Recent research suggests u…
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…
Towards Human Understanding of Paraphrase Types in Large Language Models
Dominik Meier, Jan Philip Wahle, Terry Ruas +1
Paraphrases represent a human's intuitive ability to understand expressions presented in various different ways. Current paraphrase evaluations of language models primarily use bin…
Paraphrase Types Elicit Prompt Engineering Capabilities
Jan Philip Wahle, Terry Ruas, Yang Xu +1
Much of the success of modern language models depends on finding a suitable prompt to instruct the model. Until now, it has been largely unknown how variations in the linguistic ex…