GCN-based table-to-text generation research

Zeyu Yang; Hongzhi Yu

doi:10.1117/12.3004569

16 August 2023 GCN-based table-to-text generation research

Zeyu Yang, Hongzhi Yu

Author Affiliations +

Proceedings Volume 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023); 1278712 (2023) https://doi.org/10.1117/12.3004569
Event: 6th International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE 2023), 2023, Shenyang, China

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.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zeyu Yang and Hongzhi Yu "GCN-based table-to-text generation research", Proc. SPIE 12787, Sixth International Conference on Advanced Electronic Materials, Computers, and Software Engineering (AEMCSE 2023), 1278712 (16 August 2023); https://doi.org/10.1117/12.3004569

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