Dimsum @LaySumm 20: BART-based Approach for Scientific Document Summarization
This work addresses the need to make scientific research more accessible to the general public, though it is incremental as it builds on existing BART models with added supervision.
The paper tackled the problem of automatically generating lay summaries of scientific papers by building a BART-based system that uses sentence labels as extra supervision, achieving a 46.00% Rouge1-F1 score in the CL-LaySumm 2020 shared task.
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.