Hierarchical Text Classification (HTC) is a challenging task where labels are structured in a tree or Directed Acyclic Graph (DAG) format. Current approaches often struggle with data imbalance and fail to fully capture rich semantic information. This paper proposes an Adaptive Hierarchical Text Classification method, EDTPA (ERNIE and Dynamic Threshold Pruning-based Adaptive classification), which leverages Large Language Models (LLMs) for data augmentation to mitigate imbalanced datasets. The model first uses Graph Attention Networks (GAT) to capture hierarchical dependencies among labels, effectively modeling structured relationships. ERNIE enhances the semantic representation of both the text and hierarchical labels, optimizing the model’s ability to process Chinese text. An attention mechanism strengthens the alignment between text and labels, improving accuracy. The model combines global and local information flows, while dynamic threshold pruning prunes low-probability branches, improving interpretability. Results on the Chinese Scientific Literature (CSL) dataset show EDTPA significantly outperforms baseline models in both Micro-F1 and Macro-F1 scores, effectively addressing data imbalance and improving classification performance.

Generative text summaries often suffer from factual inconsistencies, where the summary deviates from the original text. This significantly reduces their usefulness. To address this issue, we propose a novel method for improving the factual accuracy of Chinese summaries by leveraging dependency graphs. Our approach involves analyzing the input text to build a dependency graph. This graph, along with the original text, is then processed by separate models: a Relational Graph Attention Neural Network for the dependency graph and a Transformer model for the text itself. Finally, a Transformer decoder generates the summary. We evaluate the factual consistency of the generated summaries using various methods. Experiments demonstrate that our approach improves about 7.79 points compared to the baseline Transformer model on the Chinese LCSTS dataset using ROUGE-1 metric, and 4.48 points in the factual consistency assessment model StructBERT.