Automatically generates concise summaries of academic papers and documents.
Identifies and highlights main arguments, methods, results, and conclusions.
Extracts and organizes citations and references from documents.
Pulls out tables, figures, and statistical data for easy access.
Allows users to navigate through summarized content with ease.
Works with PDFs, Word documents, and other common formats.
Quickly summarizing multiple academic papers to identify trends and gaps in research.
Helping students understand complex material for exams and assignments.
Extracting key points and references to cite in academic writing and publications.
Staying updated with new publications and studies in a specific field.
Gathering relevant studies and data to support grant applications.
Assisting reviewers in efficiently assessing and summarizing papers for journals.
Creating summaries and highlights for educational materials and lectures.
Analyzing technical reports, patents, and industry documents for innovation.
Summarizing clinical trials, studies, and medical literature for healthcare professionals.
Extracting key information from legal documents, cases, and regulations.
Sign in to leave a review
Arxiv-sanity is an innovative web platform that enhances the arXiv repository by integrating machine learning for personalized paper recommendations. Created by AI researcher Andrej Karpathy, it helps academics and researchers efficiently discover and manage scientific publications. The tool uses algorithms like TF-IDF and collaborative filtering to analyze user preferences and suggest relevant papers from arXiv's vast database. Key functionalities include a personal library for saving papers, advanced search with filters by date, category, and citations, trend visualization to track popular topics, and similarity search to find related works. It is particularly valuable in fast-paced fields such as artificial intelligence, computer science, and physics, enabling users to stay updated with the latest research without manual browsing. The interface is designed for ease of use, allowing quick access to abstracts, PDFs, and metadata. By automating paper discovery, Arxiv-sanity saves time and improves research productivity, making it an essential tool for students, professionals, and enthusiasts in the scientific community.
CiteSeerX is a free, open-access digital library and search engine specifically designed for scientific publications in the fields of computer and information science. Developed and maintained by the College of Information Sciences and Technology at Pennsylvania State University, it provides researchers, students, and academics with access to millions of documents, including research papers, theses, and technical reports. The platform leverages advanced algorithms for citation indexing, allowing users to track citation networks and analyze the impact of publications. It features full-text search capabilities, metadata extraction, document clustering, and author disambiguation tools. CiteSeerX aims to enhance the discoverability and accessibility of scientific knowledge, supporting literature reviews, bibliometric studies, and research trend analysis. Its user-friendly interface and comprehensive database make it an essential resource for the computer science community, promoting open science and collaborative research.
Connected Papers is an AI-powered tool designed to assist researchers, academics, and students in visualizing and exploring connections between academic papers. By inputting a seed paper via title, DOI, or author, the tool generates an interactive graph based on citation networks, highlighting related works and seminal references. This visualization aids in comprehensive literature reviews, discovery of new research areas, identification of trends, and gap analysis. Utilizing advanced algorithms to analyze bibliographic data, Connected Papers streamlines the research process, saving time and enhancing productivity. It supports features like filtering, exporting, and customizable views, making it a valuable resource for evidence-based inquiry across various disciplines.
