@inproceedings{yang-etal-2020-textlearner,
    title = "{T}ext{L}earner at {S}em{E}val-2020 Task 10: A Contextualized Ranking System in Solving Emphasis Selection in Text",
    author = "Yang, Zhishen  and
      Wolfsteller, Lars  and
      Okazaki, Naoaki",
    editor = "Herbelot, Aurelie  and
      Zhu, Xiaodan  and
      Palmer, Alexis  and
      Schneider, Nathan  and
      May, Jonathan  and
      Shutova, Ekaterina",
    booktitle = "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
    month = dec,
    year = "2020",
    address = "Barcelona (online)",
    publisher = "International Committee for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2020.semeval-1.221/",
    doi = "10.18653/v1/2020.semeval-1.221",
    pages = "1691--1697",
    abstract = "This paper describes the emphasis selection system of the team TextLearner for SemEval 2020 Task 10: Emphasis Selection For Written Text in Visual Media. The system aims to learn the emphasis selection distribution using contextual representations extracted from pre-trained language models and a two-staged ranking model. The experimental results demonstrate the strong contextual representation power of the recent advanced transformer-based language model RoBERTa, which can be exploited using a simple but effective architecture on top."
}