A method of automatic text summarisation based on long short-term memory. (4th May 2020)

• Record Type: Journal Article
• Authors: Fang, Wei; Jiang, TianXiao; Jiang, Ke; Zhang, Feihong; Ding, Yewen; Sheng, Jack
• Is Part Of: International journal of computational science and engineering. Volume 22:Number 1(2020)
• Page Start: 39
• Page End: 49
• Publication Date: 2020-05-04
• Subjects: text summarisation -- deep NLP -- TensorFlow -- recursive neural network -- RNN -- long short-term memory -- LSTM -- Seq2Seq -- attention -- Jieba -- separate words -- language model; Computer science -- Mathematics -- Periodicals; Computer simulation -- Mathematical aspects -- Periodicals; Computational intelligence -- Periodicals; 004.015105
• Languages: English
• ISSNs: 1742-7185

Abstract

Deep learning is currently developing very fast in the NLP field and has achieved many amazing results in the past few years. Automatic text summarisation means that the abstract of the document is automatically summarised by a computer program without changing the original intention of the document. There are many application scenarios for automatic summarisation, such as news headline generation, scientific document abstract generation, search result segment generation, and product review summarisation. In the era of internet big data in the information explosion, if the short text can be employed to express the main connotation of information, it will undoubtedly help to alleviate the problem of information overload. In this paper, a model based on the long short-term memory network is presented to automatically analyse and summarise Chinese articles by using the seq2seq+attention models. Finally, the experimental results are attached and evaluated.