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20222026
most citedHow Large Language Models are Transforming Machine-Paraphrased Plagiarism

29 citations · 49 across the 10 of their papers we have counts for

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Showing 2024Show all

5 papers · 1 filter

cs.CL2024

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…

cs.CL2024★ 5 cited

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…

cs.CL2024

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…

cs.CL2024★ 10 cited

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…

cs.CL2024★ 2 cited

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…