论文标题
Semeval-2020任务10：在视觉媒体中重点选择书面文本
SemEval-2020 Task 10: Emphasis Selection for Written Text in Visual Media
论文作者
论文摘要
在本文中，我们介绍了主要发现，并比较了Semeval-2020任务10的结果，即视觉媒体中书面文本的重点选择。这项共享任务的目的是设计自动方法以重点选择，即选择重点在文本内容中的候选人，以在创作方面为自动设计帮助。主要重点是社交媒体的简短文本实例，包括各种示例，从社交媒体帖子到鼓舞人心的报价。要求参与者使用纯文本对重点进行建模，而没有用户或其他设计注意事项的其他上下文。 Semeval-2020重点选择共享任务在早期阶段吸引了197名参与者，总共有31个团队提交了这项任务。排名最高的提交获得了0.823的比赛得分。对任务提交的系统的分析表明，伯特和罗伯塔是所使用的预训练模型的最常见选择，而语音标签（POS）的一部分是最有用的功能。可以在任务的网站上找到完整的结果。
In this paper, we present the main findings and compare the results of SemEval-2020 Task 10, Emphasis Selection for Written Text in Visual Media. The goal of this shared task is to design automatic methods for emphasis selection, i.e. choosing candidates for emphasis in textual content to enable automated design assistance in authoring. The main focus is on short text instances for social media, with a variety of examples, from social media posts to inspirational quotes. Participants were asked to model emphasis using plain text with no additional context from the user or other design considerations. SemEval-2020 Emphasis Selection shared task attracted 197 participants in the early phase and a total of 31 teams made submissions to this task. The highest-ranked submission achieved 0.823 Matchm score. The analysis of systems submitted to the task indicates that BERT and RoBERTa were the most common choice of pre-trained models used, and part of speech tag (POS) was the most useful feature. Full results can be found on the task's website.
