13 citations · 65 across the 39 of their papers we have counts for
13 papers · 1 filter
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…
CiteAssist: A System for Automated Preprint Citation and BibTeX Generation
Lars Benedikt Kaesberg, Terry Ruas, Jan Philip Wahle +1
We present CiteAssist, a system to automate the generation of BibTeX entries for preprints, streamlining the process of bibliographic annotation. Our system extracts metadata, such…
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…
MAGPIE: Multi-Task Media-Bias Analysis Generalization for Pre-Trained Identification of Expressions
Tomáš Horych, Martin Wessel, Jan Philip Wahle +6
Media bias detection poses a complex, multifaceted problem traditionally tackled using single-task models and small in-domain datasets, consequently lacking generalizability. To ad…
Citation Amnesia: On The Recency Bias of NLP and Other Academic Fields
Jan Philip Wahle, Terry Ruas, Mohamed Abdalla +2
This study examines the tendency to cite older work across 20 fields of study over 43 years (1980--2023). We put NLP's propensity to cite older work in the context of these 20 othe…
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…