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:
- 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.
Now Available
Semantic Reader is now available for most arXiv papers on semanticscholar.org 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
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
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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.
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.
We present SciA11y, a system that renders inaccessible scientific paper PDFs into HTML.
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.
We introduce ScholarPhi, an augmented reading interface that brings definitions of technical terms and symbols to readers when and where they need them most.
