.png){kind=icon}
Return
Multi-modal product title compression
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ipm.2019.102123.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Product title generation in e-commerce is a challenging task, which involves modeling multimodal resources, i.e., textual descriptions and visual pictures, and comprising a sequence of words with proper ordering. Although myriad researches have studied this task and prompting progress has been made, there still exists a noticeable gap between generated titles and the requirements on mobile devices, especially considering the limited screen size. Towards filling this gap, we collect a large dataset from real e-commerce platforms to investigate compressing product titles for mobile devices, namely product title compression. We also propose a novel title compression model which takes the advantages of reinforcement learning and multi-modal resources. In doing so, our model is capable of retaining vital information in tides and improving the readability of generated titles. Experimental results demonstrate that our proposed method outperforms the state-of-the-art methods by a large margin on the automatic evaluation.
Keywords:
Multi-modal
Title compression
Attention network
Reinforcement learning
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
I
IF:
6.9
Papers:
5.2K
Citations:
1.4W
Organization
Cited Papers
ShareSaveShareSaveShareSaveShareSavePurification and characterization of oxidoreductases-catalyzing carbonyl reduction of the tobacco-specific nitrosamine 4-methylnitrosamino-1-(3-pyridyl)-1-butanone (NNK) in human liver cytosol.png)
Xenobiotica
XenobioticaIF0
PREAI
ShareSave
View more