Abstract
This study investigates semantic text segmentation enhanced by large language model (LLM) embeddings. We assess how effectively embeddings capture semantic coherence and topic closure by integrating them into both classical clustering algorithms and a modified graph-based methods. In addition, we propose a simple magnetic clustering algorithm as a lightweight baseline. Experiments are conducted across multiple datasets and embedding models, with segmentation quality evaluated using the boundary segmentation metric. Results demonstrate that LLM embeddings improve segmentation accuracy, highlight dataset-specific difficulties, and reveal how contextual window size and embedding choice affect performance. These findings clarify the strengths and limitations of embedding-based approaches to segmentation and provide insights relevant to retrieval-augmented generation (RAG).