论文标题
通过自动拉动报价选择引起注意
Catching Attention with Automatic Pull Quote Selection
论文作者
论文摘要
为了促进对读者吸引读者的理解，我们提倡自动拉动报价选择的新颖任务。拉动引号是专门设计的文章的一部分，旨在吸引读者的注意，并从文章中选择的文本跨越并提供更明显的呈现。此任务不同于相关任务，例如摘要和点击诱饵标识，但有几个方面的标识。我们为任务建立了一系列基线方法，从手工特征到专家的神经混合物到交叉任务模型。通过检查单个特征的贡献并嵌入了这些模型中的维度，我们发现了引号的意外属性，以帮助回答什么吸引读者的重要问题。人类评估还支持这项任务的独特性以及我们选择模型的适用性。进一步探索这个问题的好处是明确的：拉动引号提高了享受和可读性，形状的读者感知并促进学习。可以在https://github.com/tannerbohn/automaticpullquoteselection上获得该工作的代码。
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 https://github.com/tannerbohn/AutomaticPullQuoteSelection.
