mlsanityData to 7 Oct 2026

Envisioning the Next-Gen Document Reader

arXiv 2302.07492 ↗First posted 15 Feb 2023cs.CL cs.AI cs.HCcited by 0

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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.

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17 of 31 references.

References · 17

  1. 2018A Discourse-Aware Attention Model for Abstractive Summarization of Long Documentscited by 477
  2. 2023Benchmarking Large Language Models for News Summarizationcited by 444
  3. 2022News Summarization and Evaluation in the Era of GPT-3cited by 324
  4. 2016Image-to-Markup Generation with Coarse-to-Fine Attentioncited by 79
  5. 2020Augmenting Scientific Papers with Just-in-Time, Position-Sensitive Definitions of Terms and Symbolscited by 36
  6. 2020What Does This Acronym Mean? Introducing a New Dataset for Acronym Identification and Disambiguationcited by 26
  7. 2022Paper Plain: Making Medical Research Papers Approachable to Healthcare Consumers with Natural Language Processingcited by 23
  8. 2019A Joint Model for Definition Extraction with Syntactic Connection and Semantic Consistencycited by 14
  9. 2020SemEval-2020 Task 6: Definition extraction from free text with the DEFT corpuscited by 12
  10. 2020Acronym Identification and Disambiguation Shared Tasks for Scientific Document Understandingcited by 12
  11. 2021MadDog: A Web-based System for Acronym Identification and Disambiguationcited by 8
  12. 2022Factual Error Correction for Abstractive Summaries Using Entity Retrievalcited by 8
5 more
  1. 2022SemEval 2022 Task 12: Symlink- Linking Mathematical Symbols to their Descriptionscited by 6
  2. 2019Improving Human Text Comprehension through Semi-Markov CRF-based Neural Section Title Generationcited by 5
  3. 2020Open-Domain Question Answering with Pre-Constructed Question Spacescited by 4
  4. 2020Explain by Evidence: An Explainable Memory-based Neural Network for Question Answeringcited by 3
  5. 2022Symlink: A New Dataset for Scientific Symbol-Description Linkingcited by 2

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