Abstract
For e-commerce platforms, high-quality product titles are a vital element in facilitating transactions. A concise, accurate, and informative product title can not only stimulate consumers’ desire to buy the products, but also provide them with precise shopping guides. However, previous work is mainly based on manual rules and templates, which not only limits the generalization ability of the model, but also lacks dominant product aspects in the generated titles. In this paper, we propose a Transformer-based Multimodal Aspect-Aware Product Title Generation model, denoted as MAA-PTG, which can effectively integrate the visual and textual information of the product to generate a valuable title. Specifically, on the decoder side, we construct an image cross-attention layer to incorporate the local image feature. And then, we explore various strategies to fuse product aspects and global image features. During training, we also adopt an aspect-based reward augmented maximum likelihood (RAML) training strategy to promote our model to generate a product title covering the key product aspects. We elaborately construct an e-commerce product dataset consisting of the product-title pairs. The experimental results on this dataset demonstrate that compared with competitive methods, our MAA-PTG model has significant advantages in ROUGE score and human evaluation.
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The data that support the findings of this study are not openly available due to business sensitivity and are available from the corresponding author upon reasonable request.
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Acknowledgments
This work was supported in part by the National Natural Science Foundation of China (61862011), Guangxi Science and Technology Foundation (2019GXNSFGA245004).
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Zhang, M., Gang, Z., Yu, W. et al. MAA-PTG: multimodal aspect-aware product title generation. J Intell Inf Syst 59, 213–235 (2022). https://doi.org/10.1007/s10844-022-00695-8
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DOI: https://doi.org/10.1007/s10844-022-00695-8