Evaluating the effectiveness of large language models in meeting summarization with transcript segmentation techniques: How well does gpt-3.5-turbo perform on meeting summarization with topic and context-length window segmentation?

Large Language Models (LLM) have brought significant performance increase on many Natural Language Processing tasks. However LLMs have not been tested for meeting summarization. This research paper examines the effectiveness of the gpt-3.5-turbo model on the meeting summarization domain. However due to input length limitations, it cannot be...

Zero-shot learning for (dis)agreement detection in meeting trancripts: Comparing latent topic models and large language models

This paper presents a novel approach to detect agreement and disagreement moments between participants in meeting transcripts without relying on labeled data. We propose a model in which disagreement detection is defined as the process of first identifying argumentative theses relevant to a given corpus of text and then classifying all phrases...