Multiple studies have focused on predicting the prospective popularity of an
online document as a whole, without paying attention to the contributions of
its individual parts. We introduce the task of proactively forecasting
popularities of sentences within online news documents solely utilizing their
natural language content. We model sentence-specific popularity forecasting as
a sequence regression task. For training our models, we curate InfoPop, the
first dataset containing popularity labels for over 1.7 million sentences from
over 50,000 online news documents. To the best of our knowledge, this is the
first dataset automatically created using streams of incoming search engine
queries to generate sentence-level popularity annotations. We propose a novel
transfer learning approach involving sentence salience prediction as an
auxiliary task. Our proposed technique coupled with a BERT-based neural model
exceeds nDCG values of 0.8 for proactive sentence-specific popularity
forecasting. Notably, our study presents a non-trivial takeaway: though
popularity and salience are different concepts, transfer learning from salience
prediction enhances popularity forecasting. We release InfoPop and make our
code publicly available: this https URL

本文提出将句子级别的普及度预测作为序列回归任务，通过使用自然语言内容在在线新闻文档中预测句子的流行度，并提出一种基于 BERT 的神经模型和辅助任务中的句子重要性预测的转移学习方法。使用生成的一组句子级别的流行度注释，建立第一个可自动创建的数据集 InfoPop。研究表明，从句子重要性的预测中进行转移学习可以加强句子流行度的预测。