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It is a challenging and practical research problem to obtain effectivecompression of lengthy product titles for E-commerce. This is particularlyimportant as more and more users browse mobile E-commerce apps and moremerchants make the original product titles redundant and lengthy for SearchEngine Optimization. Traditional text summarization approaches often require alarge amount of preprocessing costs and do not capture the important issue ofconversion rate in E-commerce. This paper proposes a novel multi-task learningapproach for improving product title compression with user search log data. Inparticular, a pointer network-based sequence-to-sequence approach is utilizedfor title compression with an attentive mechanism as an extractive method andan attentive encoder-decoder approach is utilized for generating user searchqueries. The encoding parameters (i.e., semantic embedding of original titles)are shared among the two tasks and the attention distributions are jointlyoptimized. An extensive set of experiments with both human annotated data andonline deployment demonstrate the advantage of the proposed research for bothcompression qualities and online business values.

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