论文标题
使用通用添加剂模型的提取文本摘要与句子选择的交互
Extractive Text Summarization Using Generalized Additive Models with Interactions for Sentence Selection
论文作者
论文摘要
自动文本摘要（ATS）与文本数据的增长有关；但是，随着公共大规模数据集的普及，一些最近的机器学习方法集中在密集的模型和体系结构上，尽管产生了显着的结果，但通常会在难以解释的模型中出现。鉴于基于可解释的学习文本摘要的挑战及其对于不断发展ATS领域的当前状态的重要性，这项工作研究了两个具有相互作用的现代通用添加剂模型的应用，即可解释的增强机器和GAMI-NET，以及基于语言特征和二进制分类的提取性摘要问题。
Automatic Text Summarization (ATS) is becoming relevant with the growth of textual data; however, with the popularization of public large-scale datasets, some recent machine learning approaches have focused on dense models and architectures that, despite producing notable results, usually turn out in models difficult to interpret. Given the challenge behind interpretable learning-based text summarization and the importance it may have for evolving the current state of the ATS field, this work studies the application of two modern Generalized Additive Models with interactions, namely Explainable Boosting Machine and GAMI-Net, to the extractive summarization problem based on linguistic features and binary classification.
