A Simple Semantics and Topic-aware Method to Enhance Abstractive Summarization
Jiangnan Du, Xuan Fu, Jianfeng Li, Cuiqin Hou, Qiyu Zhou, Hai-Tao Zheng · 2023
In recent years, Transformer-based abstractive summarization models have been proven to be effective and widely used in multiple domains. However, most of these Transformer-based methods are based on maximum likelihood estimation and they still focus on token-level optimization. In addition, the generated summary should have the same semantics as the gold summary. Therefore, we propose a new abstractive summarization model in this paper. Specifically, we optimize the traditional model from the semantic perspective, so that the semantics of the generated summary is more similar to that of the gold summary. We also add topic information so that more key information in the text can be retained in the model. We prove in the CNN/DailyMail dataset that the method proposed in this paper greatly improve the classic abstractive model based on BART, and achieve SOTA results in the SAMSUM dataset.