Semantic Reader is an AI-powered augmented scientific reading application that enhances comprehension and contextualizes academic papers.
Semantic Reader is an augmented reader designed to improve the experience of reading scientific papers. It addresses common challenges such as navigating citations, tracking reading history, and dealing with static PDF formats. The tool uses artificial intelligence to understand a document's structure and integrates it with Semantic Scholar's academic corpus, providing detailed information through in-context tooltips and overlays. Key features include citation cards with TLDR summaries, personalized in-line citations based on user research activities, and AI-generated highlighted overlays that identify a paper's Goal, Method, and Result. It also offers on-demand definitions for words and acronyms, and integration with Hypothesis for highlighting and note-taking. The platform is available for most arXiv papers on Semantic Scholar, with a focus on English-language computer science fields. Semantic Reader is built on research from the Semantic Scholar team at AI2, UC Berkeley, and the University of Washington. It also provides open-source libraries (PaperMage and PaperCraft) and prototype demos for developers to build their own intelligent paper readers.
Integrations: Hypothesis
Platforms: Web
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