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In this paper, we focus on a new practical task, document-scale text contentmanipulation, which is the opposite of text style transfer and aims to preservetext styles while altering the content. In detail, the input is a set ofstructured records and a reference text for describing another recordset. Theoutput is a summary that accurately describes the partial content in the sourcerecordset with the same writing style of the reference. The task isunsupervised due to lack of parallel data, and is challenging to selectsuitable records and style words from bi-aspect inputs respectively andgenerate a high-fidelity long document. To tackle those problems, we firstbuild a dataset based on a basketball game report corpus as our testbed, andpresent an unsupervised neural model with interactive attention mechanism,which is used for learning the semantic relationship between records andreference texts to achieve better content transfer and better stylepreservation. In addition, we also explore the effectiveness of theback-translation in our task for constructing some pseudo-training pairs.Empirical results show superiority of our approaches over competitive methods,and the models also yield a new state-of-the-art result on a sentence-leveldataset.

Learning to Select Bi-Aspect Information for Document-Scale Text Content Manipulation