Extracting Adherence Information from Electronic Health Records

Proceedings of the 28th International Conference on Computational Linguistics, pages 62–76 Barcelona, Spain (Online), December 8-13, 2020 62 Catching Attention with Automatic Pull Quote Selection Tanner Bohn Western University London, ON, Canada tbohn@uwo.ca Charles X. Ling Western University London, ON, Canada charles.ling@uwo.ca 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 at- tention 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 further are clear: pull quotes increase enjoyment and read- ability, shape reader perceptions, and facilitate learning. Code to reproduce this work is available at https://github.com/tannerbohn/AutomaticPullQuoteSelection. 1 Introduction In a way, a PQ is like clickbait, except that it is not lying to people. Figure 1: A pull quote from this paper chosen with the help of our best perform- ing model (see Section 5.3). Discovering what keeps readers engaged is an important problem. We thus propose the novel task of automatic pull quote (PQ) selection accompanied with a new dataset and insightful analysis of several motivated baselines. PQs are graphical elements of articles with thought provoking spans of text pulled from an article by a writer or copy ed- itor and presented on the page in a more salient manner (French, 2018), such as in Figure 1. PQs serve many purposes. They provide temptation (with unusual or intriguing phrases, they make strong en- trypoints for a browsing reader), emphasis (by reinforcing particular aspects of the article), and improve overall visual balance and excitement (Stovall, 1997; Holmes, 2015). PQ frequency in reading material is also significantly related to information recall and student ratings of en- joyment, readability, and attractiveness (Wanta and Gao, 1994; Wanta and Remy, 1994). The problem of automatically selecting PQs is related to the previously studied tasks of headline success prediction (Piotrkowicz et al., 2017; Lamprinidis et al., 2018), clickbait identification (Potthast et al., 2016; Chakraborty et al., 2016; Venneti and Alam, 2018), as well as key phrase extraction (Hasan and Ng, 2014) and document summarization (Nenkova and McKeown, 2012). However, in Sections 5.4 and 5.5 we provide experimental evidence that performing well on these previous tasks does not translate to performing well at PQ selection. Each of these types of text has a different function in the context of engaging a reader. The title tells the reader what the article is about and sets the tone. Clickbait makes This …

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