 Pull quotes are an effective component of a captivating news article. These spans of text are selected from an article and provided with more salient presentation , with the aim of attracting readers with intriguing phrases and making the article more visually interesting. In this paper, we introduce the novel task of automatic pull quote selection, construct a dataset, and benchmark the performance of a number of approaches ranging from hand-crafted features to state-of-the-art sentence embeddings to cross-task models. We show that pre-trained Sentence-BERT embeddings outperform all other approaches, however the benefit over n-gram models is marginal. By closely examining the results of simple models, we also uncover many unexpected properties of pull quotes that should serve as inspiration for future approaches. We believe the benefits of exploring this problem further are clear: pull quotes have been found to increase enjoyment and readability, shape reader perceptions, and facilitate learning. 
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 further are clear: pull quotes increase enjoyment and readability, shape reader perceptions, and facilitate learning. Code to reproduce this work is available at URL
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, with the aim of attracting readers with intriguing phrases and making the article more visually interesting. In this paper, we introduce the novel task of automatic pull quote selection, construct a dataset, and benchmark the performance of a number of approaches ranging from hand-crafted features to state-of-the-art sentence embeddings
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We show that pre-trained Sentence-BERT embeddings outperform all other approaches, however the benefit over n-gram models is marginal. By closely examining the results of simple
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Code to reproduce this work is available at URL