Generating Chinese Abstracts with Content and Image Features
[Objective]This paper proposes a new Chinese abstract generation method integrating content and image features.It aims to improve the performance of existing methods based on text features.[Methods]First,we used the BERT to extract text features and used ResNet to extract image features.Then,we utilized these features to complement and validate each other.Third,we fused the two modal features with the attention mechanism.Finally,we inputted the fused features into a pointer generation network to generate higher-quality Chinese abstracts.[Results]Compared to models solely relying on single text modality,the proposed method showed improvements of 1.9％,1.3％,and 1.4％on ROUGE-1,ROUGE-2,and ROUGE-L metrics,respectively.[Limitations]The experimental data were primarily retrieved from the news domain,and the model's effectiveness in other fields remains to be verified.[Conclusions]Incorporating image information allows the fused features to retain more important information.It helps the model identify the key content better and makes the generated abstracts more comprehensive and readable.
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