A Multi-task Learning Approach for Improving Product Title Compression with User Search Log Data [article]

Jingang Wang, Junfeng Tian, Long Qiu, Sheng Li, Jun Lang, Luo Si and Man Lan
2018 arXiv   pre-print
It is a challenging and practical research problem to obtain effective compression of lengthy product titles for E-commerce. This is particularly important as more and more users browse mobile E-commerce apps and more merchants make the original product titles redundant and lengthy for Search Engine Optimization. Traditional text summarization approaches often require a large amount of preprocessing costs and do not capture the important issue of conversion rate in E-commerce. This paper
more » ... s a novel multi-task learning approach for improving product title compression with user search log data. In particular, a pointer network-based sequence-to-sequence approach is utilized for title compression with an attentive mechanism as an extractive method and an attentive encoder-decoder approach is utilized for generating user search queries. The encoding parameters (i.e., semantic embedding of original titles) are shared among the two tasks and the attention distributions are jointly optimized. An extensive set of experiments with both human annotated data and online deployment demonstrate the advantage of the proposed research for both compression qualities and online business values.
arXiv:1801.01725v1 fatcat:upfviymp6rhnxepfna7quuwa2m