  Word emphasis in textual content aims at conveying the desired intention by
changing the size, color, typeface, style (bold, italic, etc.), and other
typographical features. The emphasized words are extremely helpful in drawing
the readers' attention to specific information that the authors wish to
emphasize. However, performing such emphasis using a soft keyboard for social
media interactions is time-consuming and has an associated learning curve. In
this paper, we propose a novel approach to automate the emphasis word detection
on short written texts. To the best of our knowledge, this work presents the
first lightweight deep learning approach for smartphone deployment of emphasis
selection. Experimental results show that our approach achieves comparable
accuracy at a much lower model size than existing models. Our best lightweight
model has a memory footprint of 2.82 MB with a matching score of 0.716 on
SemEval-2020 public benchmark dataset.
