Method with Data to Text Generation Based on Selecting Encoding and Fusing Semantic Loss

Abstract

A method with data to text generation based on selecting encoding and fusing semantic loss is proposed in this paper. By highlighting key content and reducing the redundancy of text description information, the quality of the generated text is significantly improved. First, a new selection network is designed, which uses the amount of information related to data records as the coding basis for content importance, and multiple rounds of dynamic iterations of the results to achieve accurate and comprehensive selection of important information. Secondly, in the decoding process using Long Short-Term Memory (LSTM), a hierarchical attention mechanism is designed to assign dynamic selection weights to different entities and their attributes in the hidden layer output to obtain the best generated text Recall rate. Finally, a method of calculating the semantic similarity loss between the generated text and the reference text is introduced. By calculating the cosine distance of the semantic vectors of the two and iteratively feedback to the training process to obtain the optimization of key features, while reducing the redundancy of description information and improving the model BLEU performance. The experimental results show that the test Precision rate, Recall rate and BLEU is up to 94.58%, 53.72% and 17.24, which are better than existing models.