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We propose a novel domain-specific generative pre-training (DS-GPT) methodfor text generation and apply it to the product titleand review summarizationproblems on E-commerce mobile display.First, we adopt a decoder-onlytransformer architecture, which fitswell for fine-tuning tasks by combininginput and output all to-gether. Second, we demonstrate utilizing only smallamount of pre-training data in related domains is powerful. Pre-training alanguagemodel from a general corpus such as Wikipedia or the CommonCrawlrequires tremendous time and resource commitment, andcan be wasteful if thedownstream tasks are limited in variety. OurDSGPT is pre-trained on a limiteddataset, the Chinese short textsummarization dataset (LCSTS). Third, our modeldoes not requireproduct-related human-labeled data. For title summarizationtask,the state of art explicitly uses additional background knowledgeintraining and predicting stages. In contrast, our model implic-itly capturesthis knowledge and achieves significant improvementover other methods, afterfine-tuning on the public Taobao.comdataset. For review summarization task, weutilize JD.com in-housedataset, and observe similar improvement over standardmachinetranslation methods which lack the flexibility of fine-tuning.Ourproposed work can be simply extended to other domains for a widerange oftext generation tasks.

DSGPT: Domain-Specific Generative Pre-Training of Transformers for Text Generation in E-commerce Title and Review Summarization