Augmenting Scientific Papers with Just-in-Time, Position-Sensitive Definitions of Terms and Symbols

Head, Andrew; Lo, Kyle; Kang, Dongyeop; Fok, Raymond; Skjonsberg, Sam; Weld, Daniel S.; Hearst, Marti A.

doi:10.1145/3411764.3445648

Cited by 57 publications

(42 citation statements)

References 70 publications

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“…Paper Plain draws inspiration from prior affordances in interactive reading systems that have used term definitions [44],…”

Section: Interactive Reading Interfacesmentioning

confidence: 99%

“…In the context of reading research papers, Head et al [44] introduced ScholarPhi, a PDF reader that surfaces positionaware definitions for terms defined in a paper (Nonce words) and features for revealing these terms across a paper. In a usability study, researchers were able to read papers more easily using the interface.…”

Section: Interactive Reading Interfacesmentioning

confidence: 99%

“…In particular, we study how the paper itself can be imbued with new affordances to help readers navigate and evaluate its contents. As the human-computer interaction literature shows, reading interfaces can offer novel affordances to assist readers in navigating documents in new ways [6], looking up the meaning of unfamiliar terminology [44], summarizing sections as they read [12], and searching for answers to their questions [110]. Drawing on this work as inspiration, we ask what combination of affordances would be necessary to help bridge the often enormous gap between a reader's current knowledge of biomedical research and the contents of a paper.…”

Section: Introductionmentioning

confidence: 99%

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Paper Plain: Making Medical Research Papers Approachable to Healthcare Consumers with Natural Language Processing

August¹,

Wang²,

Bragg³

et al. 2022

Preprint

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When seeking information not covered in patient-friendly documents, like medical pamphlets, healthcare consumers may turn to the research literature. Reading medical papers, however, can be a challenging experience. To improve access to medical papers, we introduce a novel interactive interface-Paper Plain 1 -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 evaluate Paper Plain, finding that participants who use Paper Plain have an easier time reading and understanding research papers without a loss in paper comprehension compared to those who use a typical PDF reader. Altogether, the study results suggest that guiding readers to relevant passages and providing plain language summaries, or "gists, " alongside the original paper content can make reading medical papers easier and give readers more confidence to approach these papers. CCS Concepts: • Human-centered computing → Empirical studies in interaction design; Interactive systems and tools; User studies.

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“…Paper Plain draws inspiration from prior affordances in interactive reading systems that have used term definitions [44],…”

Section: Interactive Reading Interfacesmentioning

confidence: 99%

Section: Interactive Reading Interfacesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Paper Plain: Making Medical Research Papers Approachable to Healthcare Consumers with Natural Language Processing

August¹,

Wang²,

Bragg³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Along with commercial tools such as Adobe Reader, SideNoter (Abekawa and Aizawa, 2016) augments PDFs with rich note taking and linguistic annotation overlays, directly on the PDF canvas. ScholarPhi (Head et al, 2020) augments the PDF reading experience with equation overlays and definition modals for symbols.…”

Section: Related Workmentioning

confidence: 99%

PAWLS: PDF Annotation With Labels and Structure

Neumann¹,

Shen²,

Skjonsberg³

2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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Adobe's Portable Document Format (PDF) is a popular way of distributing view-only documents with a rich visual markup. This presents a challenge to NLP practitioners who wish to use the information contained within PDF documents for training models or data analysis, because annotating these documents is difficult. In this paper, we present PDF Annotation with Labels and Structure (PAWLS), a new annotation tool designed specifically for the PDF document format. PAWLS is particularly suited for mixed-mode annotation and scenarios in which annotators require extended context to annotate accurately. PAWLS supports span-based textual annotation, N-ary relations and freeform, non-textual bounding boxes, all of which can be exported in convenient formats for training multi-modal machine learning models.

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“…Due to the rapid growth in the number of publications of scientific articles, more and more works have recently appeared devoted to the analysis of various aspects of scientific texts. For example, the paper [10] describes an interface that makes it easier to read scientific articles by highlighting and linking definitions, variables in formulas, etc. Authors of the work [5] propose one of the approaches to the summarization of scientific texts.…”

Section: Introductionmentioning

confidence: 99%

A system for information extraction from scientific texts in Russian

Bruches¹,

Mezentseva²,

Batura³

2021

Preprint

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In this paper, we present a system for information extraction from scientific texts in the Russian language. The system performs several tasks in an end-to-end manner: term recognition, extraction of relations between terms, and term linking with entities from the knowledge base. These tasks are extremely important for information retrieval, recommendation systems, and classification. The advantage of the implemented methods is that the system does not require a large amount of labeled data, which saves time and effort for data labeling and therefore can be applied in low-and mid-resource settings. The source code is publicly available and can be used for different research purposes.

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Augmenting Scientific Papers with Just-in-Time, Position-Sensitive Definitions of Terms and Symbols

Cited by 57 publications

References 70 publications

Paper Plain: Making Medical Research Papers Approachable to Healthcare Consumers with Natural Language Processing

Paper Plain: Making Medical Research Papers Approachable to Healthcare Consumers with Natural Language Processing

PAWLS: PDF Annotation With Labels and Structure

A system for information extraction from scientific texts in Russian

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