This paper describes the system proposed for addressing the research problem posed in Task 10 of SemEval-2020: Emphasis Selection For Written Text in Visual Media. 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 the best Matchm score (described in section 2.2) of 0.810 and were ranked third on the leaderboard.
@article{arxiv.2007.10820,
title = {IITK at SemEval-2020 Task 10: Transformers for Emphasis Selection},
author = {Vipul Singhal and Sahil Dhull and Rishabh Agarwal and Ashutosh Modi},
journal= {arXiv preprint arXiv:2007.10820},
year = {2020}
}
Comments
6 pages, 3 figures, 3 tables. Accepted at Proceedings of 14th International Workshop on Semantic Evaluation (SemEval-2020)