Abstract
Text summarization is a common task in NLP. Automatic text summarization aims to transform lengthy documents into shortened versions. Recently, the neural networks based on seq2seq with attention are good at generating summarization. However, the accuracy of the summarization too difficult are to guarantee. In addition, the Out-of-Vocabulary (OOV) problem is also an important factor affecting the quality of the generated summary. To solve these problems, we hybrid the advantages of the extractive and abstractive summarization systems to propose text summarization model of combining global gated unit and copy mechanism (GGUC). The experiment results demonstrate that the performance of the model is better than the other text summary system on LCSTS datasets.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
