Menu
Introducing Semantic Reader
An AI-Powered Augmented Scientific Reading Application
Background
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:
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.
Now Available
Semantic Reader is now available for most arXiv papers on semanticscholar.org with an introductory set of features.
Work to expand coverage to more paper sources and add additional features addressing observed challenges is currently in progress
Paper Examples
Here are examples of Semantic Reader operating over popular Computer Science papers across various subfields. We are incrementally improving, testing, and rolling out new features in Semantic Reader so stay tuned. The current design is best experienced on a full-size screen.
NLP
Natural Language Processing
Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations
Google’s Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation
CV
Computer Vision
Long-term Recurrent Convolutional Networks for Visual Recognition and Description
Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution
Rethinking the Inception Architecture for Computer Vision
ML
Machine Learning
Conditional Generative Adversarial Nets
Learning Important Features Through Propagating Activation Differences
WaveNet: A Generative Model for Raw Audio
Pixel Recurrent Neural Networks
Send us your Semantic Reader feedback.
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
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 SystemsApril 29, 2022
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 SystemsApril 21, 2022
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 InterfacesMarch 22, 2022
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
TLDR
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.
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
TLDR
We present SciA11y, a system that renders inaccessible scientific paper PDFs into HTML.
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) WorkshopOctober 11, 2020
TLDR
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.
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
TLDR
We introduce ScholarPhi, an augmented reading interface that brings definitions of technical terms and symbols to readers when and where they need them most.
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
Apr 1, 2022
4 min read
New model makes academic field of study classification widely available and adds Linguistics, Law, Education, and Agriculture and Food Sciences to Semantic Scholar
Kelsey MacMillan and Sergey Feldman
Featured AI2er: Rodney Kinney
Jan 26, 2022
3 min read
Rodney Kinney is a Principal Machine Learning Engineer on the Semantic Scholar team at AI2.
Caitlin Wittlif
Semantic Scholar Academic Graph for Developers
Jan 19, 2022
2 min read
Access more than 200 million papers through the Semantic Scholar Academic Graph Dataset and APIs
Semantic Scholar
Stay Connected With Semantic Scholar
What Is Semantic Scholar?
Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI.
Learn More
About
About Us
Publishers
Blog
Careers
Product
Beta Program
S2AG API
Semantic Reader
Research
Team
Publications
Projects
Resources
Help
FAQ
Librarians
Tutorials
Contact
Proudly built by AI2 with the help of our Collaborators


Terms of Service  •  Privacy Policy
About UsPublishersBlogCareers
Beta ProgramS2AG APISemantic Reader
TeamPublicationsProjectsResources
FAQsLibrariansTutorials