@article{FreisingerSchneiderHerygersetal.2023, author = {Freisinger, Steffen and Schneider, Fabian and Herygers, Aaricia and Georges, Munir and Riedhammer, Korbinian}, title = {Unsupervised Multilingual Topic Segmentation of Video Lectures: What can Hierarchical Labels tell us about the Performance?}, editor = {Pesak, Krisztina}, doi = {10.21437/slate.2023-27}, year = {2023}, abstract = {The current shift from in-person to online education, e.g., through lecture videos, requires novel techniques for quickly searching for and navigating through media content. At this point, an automatic segmentation of the videos into thematically coherent units can be beneficial. Like in a book, the topics in an educational video are often structured hierarchically. There are larger topics, which in turn are divided into different subtopics. We thus propose a metric that considers the hierarchical levels in the reference segmentation when evaluating segmentation algorithms. In addition, we propose a multilingual, unsupervised topic segmentation approach and evaluate it on three datasets with English, Portuguese and German lecture videos. We achieve WindowDiff scores of up to 0.373 and show the usefulness of our hierarchical metric.}, language = {en} } @inproceedings{RanzenbergerBockletFreisingeretal.2024, author = {Ranzenberger, Thomas and Bocklet, Tobias and Freisinger, Steffen and Georges, Munir and Glockner, Kevin and Herygers, Aaricia and Riedhammer, Korbinian and Schneider, Fabian and Simic, Christopher and Zakaria, Khabbab}, title = {EXTENDING HANS: LARGE LANGUAGE MODELS FOR QUESTION ANSWERING, SUMMARIZATION, AND TOPIC SEGMENTATION IN AN ML-BASED LEARNING EXPERIENCE PLATFORM}, series = {Elektronische Sprachsignalverarbeitung 2024, Tagungsband der 35. Konferenz, Regensburg, 6.-8. M{\"a}rz 2024}, booktitle = {Elektronische Sprachsignalverarbeitung 2024, Tagungsband der 35. Konferenz, Regensburg, 6.-8. M{\"a}rz 2024}, publisher = {TUPress}, address = {Dresden}, isbn = {978-3-95908-325-6}, pages = {219-224}, year = {2024}, abstract = {Abstract: The use of chatbots based on large language models (LLMs) and their impact on society are influencing our learning experience platform Hochschul Assistenz-System (HAnS). HAnS uses machine learning (ML) methods to support students and lecturers in the online learning and teaching processes [1]. This paper introduces LLM-based features available in HAnS which are using the transcript of our improved Automatic Speech Recognition (ASR) pipeline with an average transcription duration of 45 seconds and an average word error rate (WER) of 6.66\% on over 8 hours of audio data of 7 lecture videos. A LLM-based chatbot could be used to answer questions on the lecture content as the ASR transcript is provided as context. The summarization and topic segmentation uses the LLM to improve our learning experience platform. We generate multiple choice questions using the LLM and the ASR transcript as context during playback in a period of 3 minutes and display them in the HAnS frontend}, language = {en} }