Abstractive summarization based on pre-trained model and knowledge enhancement

Mengqi Liu; Cuiju Luan

doi:10.1117/12.2668207

20 April 2023 Abstractive summarization based on pre-trained model and knowledge enhancement

Mengqi Liu, Cuiju Luan

Proceedings Volume 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022); 126020Z (2023) https://doi.org/10.1117/12.2668207
Event: International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 2022, Changchun, China

Abstract

Given the speedy development of Internet technology, the amount of information we get is increasing explosively, so the extraction and summary of information are extremely important. Text summarization technology, as one of the important research of natural language processing, can extract text expressing core ideas from massive data, to quickly obtain the required information. However, the traditional recurrent neural network has weak parallel computing ability, and there are cases of inconsistency with the original text content and fabrication of facts. To solve the above problems, an abstractive summary model combining a pre-trained language model and knowledge enhancement is proposed. The model incorporates the extracted factual knowledge into the summary generation process with the help of graph networks, to retain the original meaning to the greatest extent. Finally, the model is verified by standard text dataset CNN&Daily Mail and good experimental results were obtained.

Citation Download Citation

Mengqi Liu and Cuiju Luan "Abstractive summarization based on pre-trained model and knowledge enhancement", Proc. SPIE 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 126020Z (20 April 2023); https://doi.org/10.1117/12.2668207

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