Argument structure extraction (ASE) aims to identify the discourse structure
of arguments within documents. Previous research has demonstrated that
contextual information is crucial for developing an effective ASE model.
However, we observe that merely concatenating sentences in a contextual window
does not fully utilize contextual information and can sometimes lead to
excessive attention on less informative sentences. To tackle this challenge, we
propose an Efficient Context-aware ASE model (ECASE) that fully exploits
contextual information by enhancing modeling capacity and augmenting training
data. Specifically, we introduce a sequence-attention module and
distance-weighted similarity loss to aggregate contextual information and
argumentative information. Additionally, we augment the training data by
randomly masking discourse markers and sentences, which reduces the model's
reliance on specific words or less informative sentences. Our experiments on
five datasets from various domains demonstrate that our model achieves
state-of-the-art performance. Furthermore, ablation studies confirm the
effectiveness of each module in our model.

提出了一种全面利用文本上下文信息、增强建模能力和拓展训练数据的高效上下文感知论证结构模型 (ECASE)，通过引入序列注意力模块和距离加权相似度损失来聚合上下文信息和论证性信息，同时通过随机屏蔽话语标记和句子来扩充训练数据，实现了在各个领域五个数据集上的最先进性能。