Research on text summarization classification based on crowdfunding projects

In recent years, artificial intelligence technologies represented by deep learning and natural language processing have made huge breakthroughs and have begun to emerge in the field of crowdfunding project analysis. Natural language processing technology enables machines to understand and analyze the text of crowdfunding projects, and classify them based on the summary description of the project, which can help companies and individuals improve the project pass rate, so it has received widespread attention. However, most of the current researches are mostly applied to topic modeling of project texts. Few studies have proposed effective solutions for classification prediction based on abstracts of crowdfunding projects. Therefore, this paper proposes a sequence-enhanced capsule network model for this problem. Specifically, based on the work of the capsule network, we propose to connect BiGRU and CapsNet in order to achieve the effect of considering both the sequence semantic information and spatial location information of the text. We apply the proposed method to the kickstarter-NLP dataset, and the experimental results prove that our model has a good classification effect in this case.