CS-PaperSum: A Large-Scale Dataset of AI-Generated Summaries for Scientific Papers

The rapid expansion of scientific literature in computer science presents challenges in tracking research trends and extracting key insights. Existing datasets provide metadata but lack structured summaries that capture core contributions and methodologies. We introduce CS-PaperSum, a large-scale dataset of 91,919 papers from 31 top-tier computer science conferences, enriched with AI-generated structured summaries using ChatGPT. To assess summary quality, we conduct embedding alignment analysis and keyword overlap analysis, demonstrating strong preservation of key concepts. We further present a case study on AI research trends, highlighting shifts in methodologies and interdisciplinary crossovers, including the rise of self-supervised learning, retrieval-augmented generation, and multimodal AI. Our dataset enables automated literature analysis, research trend forecasting, and AI-driven scientific discovery, providing a valuable resource for researchers, policymakers, and scientific information retrieval systems.

A Survey of Graph and Attention Based Hyperspectral Image Classification Methods for Remote Sensing Data

The use of Deep Learning techniques for classification in Hyperspectral Imaging (HSI) is rapidly growing and achieving improved performances. Due to the nature of the data captured by sensors that produce HSI images, a common issue is the dimensionality of the bands that may or may not contribute to the label class distinction. Due to the widespread nature of class labels, Principal Component Analysis is a common method used for reducing the dimensionality. However,there may exist methods that incorporate all bands of the Hyperspectral image with the help of the Attention mechanism. Furthermore, to yield better spectral spatial feature extraction, recent methods have also explored the usage of Graph Convolution Networks and their unique ability to use node features in prediction, which is akin to the pixel spectral makeup. In this survey we present a comprehensive summary of Graph based and Attention based methods to perform Hyperspectral Image Classification for remote sensing and aerial HSI images. We also summarize relevant datasets on which these techniques have been evaluated and benchmark the processing techniques.