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:
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 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.
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
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
Machine Learning
Conditional Generative Adversarial Nets
Learning Important Features Through Propagating Activation Differences
WaveNet: A Generative Model for Raw Audio
Pixel Recurrent Neural Networks
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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
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
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.
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
October 17, 2021
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
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
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.
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Latest News & Updates
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Access more than 200 million papers through the Semantic Scholar Academic Graph Dataset and APIs
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