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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

What is Semantic Reader?

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

Studies have uncovered many points of friction that break the flow of comprehension when reading technical papers:

  • 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

To create a better reading experience, 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. If you’re logged-in, 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

A Revolutionary Reading Experience

Semantic Reader is now available for most arXiv papers on Semantic Scholar 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

We are incrementally improving, testing, and rolling out new features in Semantic Reader and expanding coverage to more paper sources.

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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
No items found.
  • arXiv
  • May 9, 2022
TLDR

Scim is presented, an AI-augmented reading interface designed to help researchers skim papers by automatically identifying, classifying, and highlighting salient sentences, organized into rhetorical facets rooted in common information needs.

Best Paper Award
Paper Plain: Making Medical Research Papers Approachable to Healthcare Consumers with Natural Language Processing
Tal August, Lucy Lu Wang, Jonathan Bragg, Marti A. Hearst, Andrew Head, Kyle Lo
No items found.
  • arXiv
  • 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.

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
No items found.
  • CHI
  • May 8, 2021
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.

Best Paper Award

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Latest News & Updates

New Academic Graph Datasets Released From Semantic Scholar

New Academic Graph Datasets Released From Semantic Scholar

Semantic Scholar is pleased to announce the availability of new datasets that give users access to the full range of data exposed in our API for our entire corpus.

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.