Automatic generation of Chinese abstract based on vocabulary and LSTM neural network Online publication date: Fri, 13-Nov-2020
by Guijun Zhang
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 19, No. 3, 2020
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).
Online publication date: Fri, 13-Nov-2020
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Wireless and Mobile Computing (IJWMC):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email firstname.lastname@example.org