Automatic text summarization seeks to condense source documents into fluent, informative summaries that preserve essential meaning. Two principal approaches have emerged: extractive summarization, ...

We introduce a biologically inspired bird-flocking experimental framework for text summarization that identifies the most salient sentences using contextual information, sentence position, and ...

Deep learning is a subset of machine learning that uses multi-layer neural networks to find patterns in complex, unstructured data like images, text, and audio. What sets deep learning apart is its ...

Conclusions: Text-based depression estimation models trained with standard depression labels demonstrate solid predictive performance, with embedding features, deep model architectures, and clinician ...

Google has added a 'Guided Learning' mode in Gemini to promote deeper understanding of complex topics and concepts. It's available for free to all Gemini users. You can upload your course materials, ...

This study aimed to develop a hybrid deep learning model for classifying multiple fundus diseases using ultra-widefield (UWF) images, thereby improving diagnostic efficiency and accuracy while ...

Researchers have developed a deep learning model called LSTM-SAM that predicts extreme water levels from tropical cyclones more efficiently and accurately, especially in data-scarce coastal regions, ...

Objective: This study aims to present the current state of the art on clinical text summarization using large language models, evaluate the level of evidence in existing research and assess the ...

Abstract: Text summarization is an approach by which the size of one or more document is shortened and the shorten passage presents the core information of the document. In this modern era of ...