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
this https URL

本文提出自动提取引语的任务，通过一系列的基线方法，包括手工特征、神经混合专家和跨任务模型等方法，揭示了提取引语对于阅读理解的重要性，从而为如何吸引读者提供了新的认识。