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MMAPS: End-to-End Multi-Grained Multi-Modal Attribute-Aware Product Summarization by Chen, Tao; Lin, Ze; Li, Hui; Ji, Jiayi; Zhou, Yiyi; Li, Guanbin; Ji, Rongrong is a scholarly article available to read on EtoBox.

What is MMAPS: End-to-End Multi-Grained Multi-Modal Attribute-Aware Product Summarization about?

Given the long textual product information and the product image, Multi-modal Product Summarization (MPS) aims to increase customers' desire to purchase by highlighting product characteristics with a short textual summary. Existing MPS methods can produce promising results. Nevertheless, they still 1) lack end-to-end product summarization, 2) lack multi-grained multi-modal modeling, and 3) lack multi-modal attribute modeling. To improve MPS, we propose an end-to-end multi-grained multi-modal attribute-aware product summarization method (MMAPS) for generating high-quality product summaries in e-commerce. MMAPS jointly models product attributes and generates product summaries. We design several multi-grained multi-modal tasks to better guide the multi-modal learning of MMAPS. 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. Our code is publicly available at: https://github

Author

Chen, Tao; Lin, Ze; Li, Hui; Ji, Jiayi; Zhou, Yiyi; Li, Guanbin; Ji, Rongrong

Published

2023

Language

EN