TL;DR
This paper introduces the novel task of automatic pull quote selection to enhance reader engagement, analyzing different models and features to understand what makes a quote attention-grabbing, supported by human evaluation.
Contribution
It establishes baseline approaches for automatic pull quote selection and investigates the features that contribute to engaging quotes, a previously unexplored area.
Findings
Certain features significantly influence pull quote effectiveness
Neural models outperform handcrafted feature approaches
Human evaluation confirms the models' ability to select engaging quotes
Abstract
To advance understanding on how to engage readers, we advocate the novel task of automatic pull quote selection. Pull quotes are a component of articles specifically designed to catch the attention of readers with spans of text selected from the article and given more salient presentation. This task differs from related tasks such as summarization and clickbait identification by several aspects. We establish a spectrum of baseline approaches to the task, ranging from handcrafted features to a neural mixture-of-experts to cross-task models. By examining the contributions of individual features and embedding dimensions from these models, we uncover unexpected properties of pull quotes to help answer the important question of what engages readers. Human evaluation also supports the uniqueness of this task and the suitability of our selection models. The benefits of exploring this problem…
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