Title Representation

Title representation research focuses on automatically generating, analyzing, and utilizing titles across diverse domains, aiming to improve efficiency and understanding of textual information. Current work leverages deep learning models, particularly transformer architectures like BERT and its variants, along with convolutional neural networks (CNNs) for image-based title identification, to achieve tasks such as title generation from abstracts, classifying document sentiment based on titles, and semantic similarity search for job titles. These advancements have implications for automating tasks in various fields, including scientific publishing, human resources, and information retrieval, by improving the efficiency and accuracy of title-related processes.