Electronic Science and Technology ›› 2021, Vol. 34 ›› Issue (5): 35-41.doi: 10.16180/j.cnki.issn1007-7820.2021.05.007

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Design of Text Title Generation Prototype System Based on Neural Network

ZHANG Shisen,SUN Xiankun,YIN Ling,LI Shixi   

  1. College of Electronic and Electrical Engineering,Shanghai University of Engineering Science,Shanghai 201620,China
  • Received:2020-01-22 Online:2021-05-15 Published:2021-05-24
  • Supported by:
    National Natural Science Foundation of China(61802251)


In view of the traditional manual methods cost a lot of manpower and time and can not deal with the problem of massive of non-standard texts, a prototype system of generating text titles is designed in the proposed study. In the prototype system, the non-standard text is calculated by the encoder-decoder model which is based on neural network to produce an accurate title. In the encoder part, the bidirectional long short-term memory neural network is adopted to make full use of the semantic connection between contexts. In the decoder part, one-way neural network is used for decoding operation, and attention mechanism is added to alleviate information loss and improve the effect of title generation. The evaluation indexes of ROUGE-1 and ROUGE-L obtained by experiments on LCSTS data set are 29.91 and 24.68, proving the effectiveness of the title generation prototype system.

Key words: artificial intelligence, natural language processing, neural network, title generation, prototype system, word vector, attention mechanism, generative technology

CLC Number: 

  • TP391