Authors: Guijun Zhang
Addresses: Information Institute, Shanxi Finance and Taxation College, Taiyuan, Shanxi, China
Abstract: Most methods of Chinese short text summarisation are based on extraction, and it's hard to guarantee that the abstract is consistent. In this paper, we present an effective automatic method of Chinese abstract by using vocabulary and long-short term memory neural networks. The method utilises the seq2seq architecture, and introduces the candidate vocabulary in the decoding stage, to reduce the decoder vocabulary size. Thus, the training process is faster and the result is more concise and grammatical. In the end, experimental results validate the correctness and effectiveness of the method by taking a Large-Scale Chinese Short Text Summarisation (LCSTS) data set and Recall-Oriented Understudy for Gisting Evaluation (ROUGE).
Keywords: Chinese text summarisation; Seq2Seq model; LSTM neural network.
International Journal of Wireless and Mobile Computing, 2020 Vol.19 No.3, pp.241 - 248
Received: 15 May 2019
Accepted: 19 Dec 2019
Published online: 30 Oct 2020 *