GCN-based table-to-text generation research

Abstract

Table-to-text generation is an important area of text generation, and the process of structured table-to-text generation faces problems such as the "illusion" of over-understanding table data, and the problem of selecting and ordering the content of the generated text from table data. In this paper, we present some important model mechanisms in the history of its development and a new outlook on the future development of table-to-text generation and possible technical routes. A two-stage encoder-decoder approach is analyzed as to why it is superior to its predecessors, and a new outlook is proposed for implementing table-to-text generation tasks based on graph convolutional neural networks.