Text Segmentation

Text segmentation, the task of dividing text into meaningful units, is crucial for numerous natural language processing applications, ranging from document summarization to image-based text extraction. Current research emphasizes improving segmentation accuracy and efficiency across diverse text types, including artistic text, historical documents, and spoken transcripts, often employing transformer-based models and leveraging techniques like self-supervision and weakly-supervised learning to address data scarcity. These advancements are driving progress in various fields, enabling better information retrieval, improved document understanding, and more effective processing of unstructured data.