# Dimsum @LaySumm 20: BART-based Approach for Scientific Document Summarization

arXiv: https://arxiv.org/abs/2010.09252 · HTML: https://mlsanity.com/paper/2010.09252/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2020.

## Built on by (most cited first, 1 of 1)

- [Weakly-supervised Multi-task Learning for Multimodal Affect Recognition](https://mlsanity.com/paper/2104.11560/) (2021)

## Abstract (arXiv)

Lay summarization aims to generate lay summaries of scientific papers automatically. It is an essential task that can increase the relevance of science for all of society. In this paper, we build a lay summary generation system based on the BART model. We leverage sentence labels as extra supervision signals to improve the performance of lay summarization. In the CL-LaySumm 2020 shared task, our model achieves 46.00% Rouge1-F1 score.