@inproceedings{singhal-etal-2020-iitk,
    title = "{IITK} at {S}em{E}val-2020 Task 10: Transformers for Emphasis Selection",
    author = "Singhal, Vipul  and
      Dhull, Sahil  and
      Agarwal, Rishabh  and
      Modi, Ashutosh",
    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/ingest-emnlp/2020.semeval-1.217/",
    doi = "10.18653/v1/2020.semeval-1.217",
    pages = "1665--1670",
    abstract = "We propose an end-to-end model that takes as input the text and corresponding to each word gives the probability of the word to be emphasized. Our results show that transformer-based models are particularly effective in this task. We achieved an evaluation score of 0.810 and were ranked third on the leaderboard."
}