@inproceedings{shin-etal-2020-ids,
    title = "{IDS} at {S}em{E}val-2020 Task 10: Does Pre-trained Language Model Know What to Emphasize?",
    author = "Shin, Jaeyoul  and
      Kim, Taeuk  and
      Lee, Sang-goo",
    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/jlcl-multiple-ingestion/2020.semeval-1.185/",
    doi = "10.18653/v1/2020.semeval-1.185",
    pages = "1371--1376",
    abstract = "We propose a novel method that enables us to determine words that deserve to be emphasized from written text in visual media, relying only on the information from the self-attention distributions of pre-trained language models (PLMs). With extensive experiments and analyses, we show that 1) our zero-shot approach is superior to a reasonable baseline that adopts TF-IDF and that 2) there exist several attention heads in PLMs specialized for emphasis selection, confirming that PLMs are capable of recognizing important words in sentences."
}