Emphasis Selection — Resource

Learning Emphasis Selection for Written Text in Visual Media from
Crowd-Sourced Label Distributions


  • Amirreza Shirani
  • Franck Dernoncourt
  • Paul Asente
  • Nedim Lipka
  • Seokhwan Kim
  • Jose Echevarria
  • Thamar Solorio


In visual communication, text emphasis is used to increase the comprehension of written text to convey the author’s intent. We study the problem of emphasis selection, i.e. choosing candidates for emphasis in short written text, to enable automated design assistance in authoring. Without knowing the author’s intent and only considering the input text, multiple emphasis selections are valid. We propose a model that employs end-to-end label distribution learning (LDL) on crowd-sourced data and predicts a selection distribution, capturing the inter-subjectivity (common-sense) in the audience as well as the ambiguity of the input. We compare the model with several baselines in which the problem is transformed to single-label learning by mapping label distributions to absolute labels via majority voting.

Partially Funded By :

Adobe Research

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