Textual Structure

Textual structure research focuses on understanding and leveraging the organizational patterns within text, aiming to improve information extraction, summarization, and overall comprehension by machines. Current efforts concentrate on developing robust models, including transformers and convolutional neural networks, to handle diverse text structures ranging from highly structured documents like patents to unstructured formats such as social media posts and chats. This work is crucial for advancing natural language processing capabilities, enabling more effective analysis of large-scale textual data across various domains and facilitating applications in areas like scientific literature parsing and information retrieval.