关键词: 长文档摘要, 文档压缩, 实体提示, 图神经网络, 状态空间模型

Abstract: Long-document summarization remains challenging due to complex document structures and excessive input length, which often introduce substantial noise into generative models and hinder effective knowledge acquisition. To mitigate this issue, a method integrating document compression and entity prompting was proposed to filter irrelevant content and reinforce the learning of key information. An end-to-end framework termed DCEPSum (Document Compression and Entity Prompted Summarization) was developed, in which soft-selection document compression was combined with entity prompting to generate coherent summaries. First, within the document compression module, a heterogeneous graph was constructed over sentence, entity, and section nodes, and a multi-head graph attention network was employed to aggregate contextual information and output sentence-importance weights; sentences with high weights were retained to reduce noise and enhance cross-sentence associations. Subsequently, to guide the model to focus on key entity information and reduce noise interference, a key entity selection and entity prompting mechanism was introduced. During generation, within an encoder architecture extended by a state space model, the selected entities were inserted into the input context as prefix prompts, and the final summaries were generated. Experimental results on two benchmark datasets show that the ROUGE (Recall-Oriented Understudy for Gisting Evaluation) -2 scores of DCEPSum are 23.18 and 20.86, and the ROUGE-L scores are 46.63 and 45.83; compared with the strongest baseline LSG (Local, Sparse and Global attention) (16k), ROUGE-2 increases by 0.76 and 0.67, and ROUGE-L increases by 2.31 and 3.14. The combination of document compression and entity prompting improves long-document summarization quality under controllable computational overhead and provides a feasible solution for long-context summarization modeling.

Key words: long document summarization, document compression, entity prompt, Graph Neural Network (GNN), State-Space Model (SSM)