Envisioning the Next-Gen Document Reader
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Abstract (arXiv)
People read digital documents on a daily basis to share, exchange, and understand information in electronic settings. However, current document readers create a static, isolated reading experience, which does not support users' goals of gaining more knowledge and performing additional tasks through document interaction. In this work, we present our vision for the next-gen document reader that strives to enhance user understanding and create a more connected, trustworthy information experience. We describe 18 NLP-powered features to add to existing document readers and propose a novel plug-in marketplace that allows users to further customize their reading experience, as demonstrated through 3 exploratory UI prototypes available at https://github.com/catherinesyeh/nextgen-prototypes
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 31 references that are in the map. First posted on arXiv 15 Feb 2023.
Builds on
17 of 31 references.
References · 17
- 2018A Discourse-Aware Attention Model for Abstractive Summarization of Long Documentscited by 477
- 2023Benchmarking Large Language Models for News Summarizationcited by 444
- 2022News Summarization and Evaluation in the Era of GPT-3cited by 324
- 2016Image-to-Markup Generation with Coarse-to-Fine Attentioncited by 79
- 2020Augmenting Scientific Papers with Just-in-Time, Position-Sensitive Definitions of Terms and Symbolscited by 36
- 2020What Does This Acronym Mean? Introducing a New Dataset for Acronym Identification and Disambiguationcited by 26
- 2022Paper Plain: Making Medical Research Papers Approachable to Healthcare Consumers with Natural Language Processingcited by 23
- 2019A Joint Model for Definition Extraction with Syntactic Connection and Semantic Consistencycited by 14
- 2020SemEval-2020 Task 6: Definition extraction from free text with the DEFT corpuscited by 12
- 2020Acronym Identification and Disambiguation Shared Tasks for Scientific Document Understandingcited by 12
- 2021MadDog: A Web-based System for Acronym Identification and Disambiguationcited by 8
- 2022Factual Error Correction for Abstractive Summaries Using Entity Retrievalcited by 8
5 more
- 2022SemEval 2022 Task 12: Symlink- Linking Mathematical Symbols to their Descriptionscited by 6
- 2019Improving Human Text Comprehension through Semi-Markov CRF-based Neural Section Title Generationcited by 5
- 2020Open-Domain Question Answering with Pre-Constructed Question Spacescited by 4
- 2020Explain by Evidence: An Explainable Memory-based Neural Network for Question Answeringcited by 3
- 2022Symlink: A New Dataset for Scientific Symbol-Description Linkingcited by 2
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