# IITK at SemEval-2020 Task 10: Transformers for Emphasis Selection

arXiv: https://arxiv.org/abs/2007.10820 · HTML: https://mlsanity.com/paper/2007.10820/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2020.

## Built on by (most cited first, 2 of 2)

- [Learning to Emphasize: Dataset and Shared Task Models for Selecting Emphasis in Presentation Slides](https://mlsanity.com/paper/2101.03237/) (2021)
- [Cisco at AAAI-CAD21 shared task: Predicting Emphasis in Presentation Slides using Contextualized Embeddings](https://mlsanity.com/paper/2101.11422/) (2021)

## Abstract (arXiv)

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.