标题中的钩子：学习用受控的风格生成标题
Hooks in the Headline: Learning to Generate Headlines with Controlled Styles
摘要：
目前的摘要系统只能产生简单、真实的标题，但不能满足创建令人难忘的标题以增加曝光的实际需求。我们提出了一个新的任务，文体标题生成（SHG），以丰富的标题与三种风格的选择（幽默，浪漫和点击诱饵），以吸引更多的读者。没有特定风格的文章标题对（只有标准的标题摘要数据集和单风格语料库），我们的方法TitleStylist通过将摘要和重建任务结合到多任务框架中来生成特定风格的标题。我们还引入了一个新的参数共享方案，以进一步从文本中解开风格。通过自动和人工评估，我们证明了TitleStylist可以生成相关的，流畅的标题与三个目标风格：幽默，浪漫和clickbait。我们的模型生成的标题的吸引力得分超过了最先进的摘要模型9.68%，甚至超过了人类撰写的参考文献。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
