论文标题
标题中的钩子：学习以受控样式生成标题
Hooks in the Headline: Learning to Generate Headlines with Controlled Styles
论文作者
论文摘要
当前的摘要系统只会产生简单的事实头条，但不能满足创建令人难忘的标题以增加曝光率的实际需求。我们提出了一项新任务，即风格的标题生成（SHG），以通过三种样式选项（幽默，浪漫和签署）丰富头条新闻，以吸引更多的读者。由于没有特定于样式的文章头条对（仅标准标题摘要数据集和单式语料库），我们的方法titlestylist通过将汇总和重建任务组合到多任务框架中来生成特定于样式的头条新闻。我们还引入了一种新颖的参数共享方案，以进一步将样式与文本相关。通过自动评估和人类评估，我们证明了TitleStylist可以以三种目标样式产生相关的流利头条：幽默，浪漫和点击诱饵。我们模型的吸引力得分超过了最新的摘要模型的标题9.68％，甚至胜过人为所写的参考。
Current summarization systems only produce plain, factual headlines, but do not meet the practical needs of creating memorable titles to increase exposure. We propose a new task, Stylistic Headline Generation (SHG), to enrich the headlines with three style options (humor, romance and clickbait), in order to attract more readers. With no style-specific article-headline pair (only a standard headline summarization dataset and mono-style corpora), our method TitleStylist generates style-specific headlines by combining the summarization and reconstruction tasks into a multitasking framework. We also introduced a novel parameter sharing scheme to further disentangle the style from the text. Through both automatic and human evaluation, we demonstrate that TitleStylist can generate relevant, fluent headlines with three target styles: humor, romance, and clickbait. The attraction score of our model generated headlines surpasses that of the state-of-the-art summarization model by 9.68%, and even outperforms human-written references.
