13 citations · 68 across the 49 of their papers we have counts for
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Automated Collection of Evaluation Dataset for Semantic Search in Low-Resource Domain Language
Anastasia Zhukova, Christian E. Matt, Bela Gipp
Domain-specific languages that use a lot of specific terminology often fall into the category of low-resource languages. Collecting test datasets in a narrow domain is time-consumi…
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…
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…
Text Generation: A Systematic Literature Review of Tasks, Evaluation, and Challenges
Jonas Becker, Jan Philip Wahle, Bela Gipp +1
Text generation has become more accessible than ever, and the growing interest in these systems, especially those using large language models, has spurred a surge in related public…
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…
Can LLMs Master Math? Investigating Large Language Models on Math Stack Exchange
Ankit Satpute, Noah Giessing, Andre Greiner-Petter +4
Large Language Models (LLMs) have demonstrated exceptional capabilities in various natural language tasks, often achieving performances that surpass those of humans. Despite these…
The Media Bias Taxonomy: A Systematic Literature Review on the Forms and Automated Detection of Media Bias
Timo Spinde, Smi Hinterreiter, Fabian Haak +4
The way the media presents events can significantly affect public perception, which in turn can alter people's beliefs and views. Media bias describes a one-sided or polarizing per…