Given the long textual product information and the product image, Multi-Modal
Product Summarization (MMPS) aims to attract customers' interest and increase
their desire to purchase by highlighting product characteristics with a short
textual summary. Existing MMPS methods have achieved promising performance.
Nevertheless, there still exist several problems: 1) lack end-to-end product
summarization, 2) lack multi-grained multi-modal modeling, and 3) lack
multi-modal attribute modeling. To address these issues, we propose an
end-to-end multi-grained multi-modal attribute-aware product summarization
method (M3PS) for generating high-quality product summaries in e-commerce. M3PS
jointly models product attributes and generates product summaries. Meanwhile,
we design several multi-grained multi-modal tasks to better guide the
multi-modal learning of M3PS. Furthermore, we model product attributes based on
both text and image modalities so that multi-modal product characteristics can
be manifested in the generated summaries. Extensive experiments on a real
large-scale Chinese e-commence dataset demonstrate that our model outperforms
state-of-the-art product summarization methods w.r.t. several summarization
metrics.

提出了一种多模态属性感知的端到端多粒度多模态产品摘要方法 (M3PS)，用于在电子商务中生成高质量的产品摘要，并且能够克服现有的产品摘要方法存在的问题。