Low Resource Summarization Model Based on Latent Structural Semantic En-hancement - LIU Yu, LIU Xiaoming, LIU Weiguang, YANG Guan, LIU Jie (Jisuanji kexue yu tansuo, 2023)
At present, low-resource summary generation tasks are usually processed by data enhancement or pre-training combined with fine-tuning, which cannot make full use of the latent structural semantic information between the source text and the target summary. For this reason, this paper proposes a low resource summary model based on latent structural semantic enhancement, which enhances the utilization of structured information in the way of graph structure alignment. First of all, the model obtains the latent semantic features of the source text and prediction summary through the structural feature representation layer. Then, the obtained semantic features are aligned with the latent structured alignment module for node alignment and edge alignment, which helps the model to capture the structured information in the semantic features, thus enhancing the model??s use of structured knowledge. Finally, the model uses the structured feature alignment distance between the source text and the prediction summary as the regular term of target loss to assist the model in optimization. Experiments are performed on a low-resource dataset across six domains. The model achieves an average improvement of 0.58 in ROUGE-1 scores relative to the baseline model. The results show that the model can effectively improve the ability of generating low-resource summaries by using latent structured semantic knowledge.