Publikationsserver der Technischen Hochschule Augsburg
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Consumer Health Search und die Authentizität von Informationen – Eine multidimensionale Betrachtung
No full textWave-edge Modeling Method for Segmented Timber Plate Shell Structures: A Computational Tool for Optimizing the Bonding Area of CLT Joints
No full textSelection of Representative Urban Low-voltage Grids for Electric Vehicle Integration Studies
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Methodology for Identification of Controller Parameters of a Diesel Generator in Islanded Grid Operation
No full textEduQuick: A Dataset Toward Evaluating Summarization of Informal Educational Content for Social Media
No full textThis study explores the capacity of large language models (LLMs) to efficiently generate summaries of informal educational content tailored for platforms like TikTok. It also investigates how both humans and LLMs assess the quality of these summaries, based on a series of experiments, exploring the potential replacement of human evaluation with LLMs. Furthermore, the study delves into how experienced content creators perceive the utility of automatic summaries for TikTok videos. We employ strategic prompt selection techniques to guide LLMs in producing engaging summaries based on the characteristics of viral TikTok content, including hashtags, captivating hooks, storytelling, and user engagement. The study leverages OpenAI’s GPT-4 model to generate TikTok content summaries, aiming to align them with the essential features identified. By employing this model and incorporating human evaluation and expert assessment, this research endeavors to shed light on the intricate dynamics of modern content creation, where AI and human ingenuity converge. Ultimately, it seeks to enhance strategies for disseminating and evaluating educational information effectively in the realm of social media
