Dimsum @LaySumm 20: BART-based Approach for Scientific Document Summarization
arXiv:2010.09252
Abstract
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.
arXiv comment4 pages
References in corpus (9)
- Teaching Machines to Read and Comprehend
- SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents
- Text Summarization with Pretrained Encoders
- Ask the Right Questions: Active Question Reformulation with Reinforcement Learning
- Get To The Point: Summarization with Pointer-Generator Networks
- Reading Wikipedia to Answer Open-Domain Questions
- Data Augmentation for BERT Fine-Tuning in Open-Domain Question Answering
- On Extractive and Abstractive Neural Document Summarization with Transformer Language Models
- Scientific Article Summarization Using Citation-Context and Article's Discourse Structure