# Modeling Semantic-Aware Prompt-Based Argument Extractor in Documents
## 2 Related Work
### Document-Level Event Argument Extraction  
Currently, there are four main strategies in the research methodology for document-level event argument extraction (DocEAE).  
1. Traditional classification-based approaches: determining whether a candidate argument acts as an argument for a role by generating candidate arguments and making classification judgments for each role, e.g., [PERSON] et al. [31] modeled chapter semantics using Abstract Semantic Representation Graphs (AMRs), and [PERSON] et al. [32] used the STCP methodology to introduce role correlations to enhance accuracy. In addition, [PERSON] et al. [33] incorporated knowledge distillation with association modeling to improve the capture of argument role dependencies in event structures, thereby enhancing overall model performance.
2. Span selection-based approaches: avoid the complexity of candidate generation by selecting the text span of an argument directly in the chapter. For example, [PERSON] et al. [34] designed a hint template. This template generates two span selectors for individual characters, which are designed to capture the boundary positions of arguments. [PERSON] et al. [35] extended the PAIE approach by introducing soft prompts to more flexibly utilize contextual information. [PERSON] et al. [36] went further by constructing a network of dependency-aware graphs within and between events to model the role dependencies in events. [PERSON] et al. [37] captured long-distance dependencies through a sparse attention mechanism, which effectively improved the extraction accuracy and efficiency. [PERSON] et al. [38] proposed a hyperspherical multi-prototype model with optimal transport to assign arguments to prototypes, guiding the learning of argument representations for event argument extraction.
3. Machine Reading Comprehension (MRC)-based approach: the task is converted to machine reading comprehension. Argument extraction is achieved by asking questions and identifying the answers in the text. For example, [PERSON] et al. [39] enhanced the inference ability of the model by capturing the semantic relationships between arguments and arguments by using other arguments and their roles in the same event as clues.
4. Text generation-based approach: the task is formulated as text generation to realize event argument extraction. [PERSON] et al. [40] extended the generative model to capture the association semantics between multiple events. [PERSON] et al. [41] incorporated retrieval enhancement techniques into the generative model for better generation of argument information, which provides diversified solution ideas for event argument extraction.  
Although these strategies have made progress, they still face certain limitations. Traditional generative frameworks, while effective in modeling complex event semantic relationships, often employ autoregressive mechanisms that rely on sequence order, limiting the efficiency of parallel processing of multiple events. This sequential dependency hampers the model's ability to handle long-distance dependencies and complicates the extraction of arguments spanning multiple sentences. To address these issues, a document-sentence-entity heterogeneous graph is constructed and graph convolutional networks (GCNs) are employed to model global semantic associations, enabling more efficient handling of dispersed information. Additionally, a position-aware semantic role (SRL) attention mechanism is proposed to strengthen the association between semantic and positional information, improving argument extraction accuracy and the model's ability to manage complex event relationships in documents.