A Review Summary Generation Model with Emotion-Topic Dual-Channel Information
[Objective]This paper aims to solve the problem that traditional automatic summarization technology cannot deeply integrate emotion and topic information synthetically,and cannot solve the lexical deficiency,a review summary generation model integrating emotion and topic information is proposed.[Methods]TextRank is used to dynamically extract the comment topic sentence,and PyABSA model is used to extract the aspect word-emotion word sequence in the topic sentence to concatenate the topic sentence to obtain the final topic information.The emotion sentence is obtained by constructing the emotion word set and Bi-LSTM emotion word extraction model integrating the topic,and the comment text and emotion sentence are concatenated to form dual-channel information with the topic sentence.The attention mechanism is used to obtain topic attention and emotion attention,respectively,and the superposition of them is deeply fused to obtain fusion attention.The single-channel attention of the pointer generation network is replaced,and the final comment summary is generated by the pointer network.[Results]Compared with the comparative experiment Topic+PNG,the proposed pointer generation network with dual-channel information improves the ROUGE-1,ROUGE-2 and ROUGE-L values by 2.87％,6.14％and 2.64％,respectively.The ablation experiment showed that ROUGE-1,ROUGE-2 and ROUGE-L value of integrating dual-channel information were 4.49％,3.66％and 4.16％higher than single-channel information.[Limitations]Because fine-grained attribute words may appear in comments,the integration of fine-grained attributes is not considered.[Conclusions]The model can effectively integrate the topic information and emotion information of the comments,improve the quality of the two-channel information fusion,and outperform the comparison model in the summary generation results.The generated summary can contain more emotion and topic information.
万方数据
维普
