Heterogeneous Graph Opinion Summary Method Which Introduced Topic Nodes
Social applications such as microblogging carry different views of internet users on social opinion events,and how to identify valuable information in the massive amount of thematic comments has become an important issue.An opinion summarization method based on heterogeneous graphs was proposed,which effectively extracted the prevailing viewpoints of hot public opinion events to facilitate the guidance of resolving internet public opinion crises.In order to address the challenging problem of capturing cross-document semantic relationships in the multi-document summarization task,topic nodes were introduced into the comment sentence graph to mine the potential semantic associations among the input documents.Specifically,the topics of comments were extracted to construct a heterogeneous graph model where graph attention mechanism was used to interact with the semantic information of nodes at different granularities,and finally,the maximum bounded correlation algorithm was combined to extract candidate summary sentences.The results show that the improved model improves the Rougel,Rouge2,and RougeL scores by 0.46％,0.46％,and 0.48％on the English general Multi-News dataset respectively.Comparing with the existing hotspot models such as Textrank,Sumpip and so on,the model achieves the best performance on the self-made microblog comment dataset.
万方数据
维普
