You Are All Set!

You now have access to Semantic Reader Beta features including highlighting and note taking.

Illustration: Semantic Reader example showing how citations can be viewed in context of the rest of the paper.
Semantic Reader

Introducing Semantic Reader

An AI-Powered Augmented Scientific Reading Application


Semantic Reader is an augmented reader with the potential to revolutionize scientific reading by making it more accessible and richly contextual.

Studies of scientists reading technical papers show that readers are subject to many points of friction that break the flow of paper comprehension:

  • Frequently paging back and forth looking for the details of cited papers
  • Challenges recognizing the same work across multiple papers
  • Losing track of reading history and notes
  • Contending with a PDF format that is not well suited to mobile reading or assistive technologies such as screen readers

Semantic Reader uses artificial intelligence to understand a document’s structure and merge it with the Semantic Scholar’s academic corpus, providing detailed information in context via tooltips and other overlays. For readers that log into Semantic Scholar, Semantic Reader integrates with your library and, over time, will incorporate personalized contextual augmentations as well.

Semantic Reader interface showing citation detail cards, Table of Contents, Save to Library button, and Cite button

Now Available

Semantic Reader is now available for most arXiv papers on with an introductory set of features.

  • Citations Cards that show details of a cited paper in-line where you’re reading, including TLDR summaries
  • Table of Contents to quickly navigate between sections (availability varies)
  • Save to Library to conveniently track your reading list

Work to expand coverage to more paper sources and add additional features addressing observed challenges is currently in progress

Powered by State-of-the-Art Research

Semantic Reader is based on research from the Semantic Scholar team at AI2, UC Berkeley and the University of Washington, and supported in part by the Alfred P. Sloan Foundation.

Scim: Intelligent Faceted Highlights for Interactive, Multi-Pass Skimming of Scientific Papers
Raymond Fok, Andrew Head, Jonathan Bragg, Kyle Lo, Marti A. Hearst, Daniel S. Weld
  • ArXiv
  • May 9, 2022

Best Paper Award
Math Augmentation: How Authors Enhance the Readability of Formulas using Novel Visual Design Practices
Andrew Head, Amber Xie, Marti A. Hearst
  • CHI Conference on Human Factors in Computing Systems
  • April 29, 2022

Best Paper Award
From Who You Know to What You Read: Augmenting Scientific Recommendations with Implicit Social Networks
Hyeonsu Kang, Rafal Kocielnik, Andrew Head, Jiangjiang Yang, Matt Latzke, A. Kittur, Daniel S. Weld, Doug Downey, Jonathan Bragg
  • CHI Conference on Human Factors in Computing Systems
  • April 21, 2022

Best Paper Award
CiteRead: Integrating Localized Citation Contexts into Scientific Paper Reading
Napol Rachatasumrit, Jonathan Bragg, Amy X. Zhang, Daniel S. Weld
  • 27th International Conference on Intelligent User Interfaces
  • March 22, 2022

Best Paper Award
Paper Plain: Making medical research papers approachable to healthcare consumers with natural language processing
Tal August, L. Wang, Jonathan Bragg, Marti A. Hearst, Andrew Head, Kyle Lo
  • Preprint
  • February 28, 2022

To improve access to medical papers, we introduce a novel interactive interface-Paper Plain-with four features powered by natural language processing: definitions of unfamiliar terms, in-situ plain language section summaries, a collection of key questions that guide readers to answering passages, and plain language summaries of the answering passages.

Best Paper Award
SciA11y: Converting Scientific Papers to Accessible HTML
Lucy Lu Wang, Isabel Cachola, Jonathan Bragg, Evie (Yu-Yen) Cheng, Chelsea Hess Haupt, Matt Latzke, Bailey Kuehl, Madeleine van Zuylen, Linda M. Wagner, Daniel S. Weld
  • ASSETS Demo
  • October 17, 2021

We present SciA11y, a system that renders inaccessible scientific paper PDFs into HTML.

Best Paper Award
Document-Level Definition Detection in Scholarly Documents: Existing Models, Error Analyses, and Future Directions
Dongyeop Kang, Andrew Head, Risham Sidhu, Kyle Lo, Daniel S. Weld, Marti A. Hearst
  • EMNLP; Scholarly Document Processing (SDP) Workshop
  • October 11, 2020

The task of definition detection is important for scholarly papers, because papers often make use of technical terminology that may be unfamiliar to readers. We develop a new definition detection system, HEDDEx, that utilizes syntactic features, transformer encoders, and heuristic filters, and evaluate it on a standard sentence-level benchmark.

Best Paper Award
Augmenting Scientific Papers with Just-in-Time, Position-Sensitive Definitions of Terms and Symbols
Andrew Head, Kyle Lo, Dongyeop Kang, Raymond Fok, Sam Skjonsberg, Daniel S. Weld, Marti A. Hearst
  • CHI
  • September 29, 2020

We introduce ScholarPhi, an augmented reading interface that brings definitions of technical terms and symbols to readers when and where they need them most.

Best Paper Award

Experience a smarter way to search and discover scholarly research.

Create Your Account

Latest News & Updates

Announcing S2FOS, an open source academic field of study classifier

Announcing S2FOS, an open source academic field of study classifier

New model makes academic field of study classification widely available and adds Linguistics, Law, Education, and Agriculture and Food Sciences to Semantic Scholar

Featured AI2er: Rodney Kinney

Featured AI2er: Rodney Kinney

Rodney Kinney is a Principal Machine Learning Engineer on the Semantic Scholar team at AI2.

Semantic Scholar Academic Graph for Developers

Semantic Scholar Academic Graph for Developers

Access more than 200 million papers through the Semantic Scholar Academic Graph Dataset and APIs