Graph Neural Network and BERT-based Semantic Comprehension Method for Automatic Abstraction of Long Texts

With the advent of the information explosion era, how to obtain the core content of long text quickly and accurately has become a research hotspot. With traditional automatic summarization methods, it is often difficult to capture the deep semantic relationships and long-distance dependencies in the text, resulting in poor quality of the generated summarization. In this paper, based on Graph Neural Network (GNN) and Bidirectional Encoder Representations from Transformers (BERT), an automatic summarization model for long text is constructed. First, long text is divided into text embedding representation to obtain rich context information. The hierarchical decomposition position embedding and Convolutional Neural Network (CNN) are adopted to pre-train BERT model. Furthermore, in order to capture the global and local semantic perception information of text, the GNN model based on topic algorithm and BiLSTM is integrated to construct a new model that can accurately identify the overall structure and context of text and realize more accurate automatic summarization of long text. Extensive evaluation was performed simultaneously on multiple long text datasets and compared with other summarization methods. The experimental results show that the proposed method can significantly improve the performance of the long text automatic summarization task, which not only improves the accuracy of the summarization, but also better preserves the key information in the original text. The research results not only provide a new technical path for the field of automatic summarization, but also provide a new idea for long text processing and analysis.