融合词、句层级信息的抽取式摘要优化框架

A Hybrid Optimization Framework Fusing Word-and Sentence-Level Information for Extractive Summarization

提出一个混合的抽取式摘要优化框架,在优化单词层级信息的同时,将句子层级信息作为优化约束。在约束条件下,该优化框架迭代地进行摘要文本中单元的替换,得到不断逼近目标函数的最优解。与传统方法对比,该框架在DUC数据集上获得ROUGE评测的高分,证明了该框架的有效性。

In order to fuse word-level and sentence-level information from different semantic spaces,the authors propose a hybrid optimization framework to optimize word-level information while simultaneously incorporate sentence-level information as constraints.The optimization is conducted by iterative unit substitutions.The performance on DUC benchmark datasets demonstrates the effectiveness of proposed framework in terms of ROUGE evaluation.