HiText: Text reading with dynamic salience marking

Qian Yang, Sen Wang, Yong Cheng, Gerard De Melo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

The staggering amounts of content readily available to us via digital channels can often appear overwhelming. While much research has focused on aiding people at selecting relevant articles to read, only few approaches have been developed to assist readers in more efficiently reading an individual text. In this paper, we present HiText, a simple yet effective way of dynamically marking parts of a document in accordance with their salience. Rather than skimming a text by focusing on randomly chosen sentences, students and other readers can direct their attention to sentences determined to be important by our system. For this, we rely on a deep learning-based sentence ranking method. Our experiments show that this results in marked increases in user satisfaction and reading efficiency, as assessed using TOEFL-style reading comprehension tests.

Original languageEnglish (US)
Title of host publication26th International World Wide Web Conference 2017, WWW 2017 Companion
PublisherInternational World Wide Web Conferences Steering Committee
Pages311-319
Number of pages9
ISBN (Electronic)9781450349147
DOIs
StatePublished - 2017
Event26th International World Wide Web Conference, WWW 2017 Companion - Perth, Australia
Duration: Apr 3 2017Apr 7 2017

Publication series

Name26th International World Wide Web Conference 2017, WWW 2017 Companion

Other

Other26th International World Wide Web Conference, WWW 2017 Companion
Country/TerritoryAustralia
CityPerth
Period4/3/174/7/17

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Networks and Communications

Keywords

  • Natural language semantics
  • Text skimming
  • Text visualization

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