Summarization of texts have been considered as essential practice nowadays with the careful presentation of the main ideas of a text. The current study aims to provide a methodology of summarizing ...

Grace is a Guides Staff Writer from New Zealand with a love for fiction and storytelling. Grace has been playing games since childhood and enjoys a range of different genres and titles. From pick your ...

A feature summarization approach, leveraging TF-IDF and K-means clustering, was employed to extract and distill key radiological findings related to three diseases. Simultaneously, the hybrid RAG ...

Introduction: Plant phenotyping is a critical area in agricultural research that focuses on assessing plant traits quantitatively to enhance productivity and sustainability. While traditional methods ...

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 ...

Video captioning models are typically trained on datasets consisting of short videos, usually under three minutes in length, paired with corresponding captions. While this enables them to describe ...

Abstract: This project focused on using clinical text data from the PubMed dataset to train transformer models and deep learning models for text summarization. The primary goal was to develop a system ...

Sometime in the 1960s, hypertext pioneer Ted Nelson envisioned deep linking to specific pieces of text as a core feature of his proposed Project Xanadu system. (My first exposure to Xanadu came in the ...