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Existing data-to-text generation efforts mainly focus on generating acoherent text from non-linguistic input data, such as tables andattribute-value pairs, but overlook that different application scenarios mayrequire texts of different styles. Inspired by this, we define a new task,namely stylized data-to-text generation, whose aim is to generate coherent textfor the given non-linguistic data according to a specific style. This task isnon-trivial, due to three challenges: the logic of the generated text,unstructured style reference, and biased training samples. To address thesechallenges, we propose a novel stylized data-to-text generation model, namedStyleD2T, comprising three components: logic planning-enhanced data embedding,mask-based style embedding, and unbiased stylized text generation. In the firstcomponent, we introduce a graph-guided logic planner for attribute organizationto ensure the logic of generated text. In the second component, we devisefeature-level mask-based style embedding to extract the essential style signalfrom the given unstructured style reference. In the last one, pseudo tripletaugmentation is utilized to achieve unbiased text generation, and amulti-condition based confidence assignment function is designed to ensure thequality of pseudo samples. Extensive experiments on a newly collected datasetfrom Taobao have been conducted, and the results show the superiority of ourmodel over existing methods.

Stylized Data-to-Text Generation: A Case Study in the E-Commerce Domain