CUSTOM: Aspect-Oriented Product Summarization for E-Commerce
This work addresses the need for personalized product descriptions in e-commerce, offering a more tailored approach compared to conventional systems, though it is incremental as it builds on existing sequence-to-sequence models.
The paper tackles the problem of generating general product summaries that may not align with customer interests by proposing CUSTOM, an aspect-oriented product summarization system for e-commerce, which generates diverse and controllable summaries for different product aspects, achieving high-quality results as demonstrated on two new Chinese datasets.
Product summarization aims to automatically generate product descriptions, which is of great commercial potential. Considering the customer preferences on different product aspects, it would benefit from generating aspect-oriented customized summaries. However, conventional systems typically focus on providing general product summaries, which may miss the opportunity to match products with customer interests. To address the problem, we propose CUSTOM, aspect-oriented product summarization for e-commerce, which generates diverse and controllable summaries towards different product aspects. To support the study of CUSTOM and further this line of research, we construct two Chinese datasets, i.e., SMARTPHONE and COMPUTER, including 76,279 / 49,280 short summaries for 12,118 / 11,497 real-world commercial products, respectively. Furthermore, we introduce EXT, an extraction-enhanced generation framework for CUSTOM, where two famous sequence-to-sequence models are implemented in this paper. We conduct extensive experiments on the two proposed datasets for CUSTOM and show results of two famous baseline models and EXT, which indicates that EXT can generate diverse, high-quality, and consistent summaries.