GraphLSS：融合词汇、结构与语义特征的长文档抽取式摘要生成
GraphLSS: Integrating Lexical, Structural, and Semantic Features for Long Document Extractive Summarization
AI总结：
针对现有异构图神经网络长文档摘要模型依赖外部工具、结构复杂的问题，提出融合词汇、结构、语义特征的GraphLSS异构图构建方法，无需辅助模型，在两个基准数据集上性能比肩顶尖图方法、优于近期非图模型，且已开源代码。
摘要：
异构图神经网络近年来因用于长文档摘要生成而受到关注，该方法将摘要抽取建模为节点分类任务。这类模型虽有效，但通常需要外部工具或额外的机器学习模型来定义图组件，导致结构高度复杂且直观性不足。我们提出GraphLSS，一种面向长文档抽取式摘要的异构图构建方法，融合了词汇（Lexical）、结构（Structural）与语义（Semantic）特征。它定义了两级信息（词和句子）与四类边（句子语义相似度、句子出现顺序、词在句中、词语义相似度），无需任何辅助学习模型。在两个基准数据集上的实验表明，GraphLSS性能与顶尖的基于图的方法相当，优于近期的非图模型。我们已在GitHub上发布代码。
英文摘要：
Heterogeneous graph neural networks have recently gained attention for long document summarization, modeling the extraction as a node classification task. Although effective, these models often require external tools or additional machine learning models to define graph components, producing highly complex and less intuitive structures. We present GraphLSS, a heterogeneous graph construction for long document extractive summarization, incorporating Lexical, Structural, and Semantic features. It defines two levels of information (words and sentences) and four types of edges (sentence semantic similarity, sentence occurrence order, word in sentence, and word semantic similarity) without any need for auxiliary learning models. Experiments on two benchmark datasets show that GraphLSS is competitive with top-performing graph-based methods, outperforming recent non-graph models. We release our code on GitHub.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
