Insertion of Ontological Knowledge to Improve Automatic Summarization Extraction Methods

• 作者: Jésus Antonio Motta Laurence Capus Nicole Tourigny
• 来源期刊: 智能学习系统与应用(英文)
• 学科: 医学
• 关键词: Automatic SUMMARIZATION ONTOLOGY MACHINE Learning Extraction Method
• 年，卷（期）: 2011,（3）
• 页码范围: 131-138
• 页数: 8页
• 分类号: R73

摘要

The vast availability of information sources has created a need for research on automatic summarization. Current methods perform either by extraction or abstraction. The extraction methods are interesting, because they are robust and independent of the language used. An extractive summary is obtained by selecting sentences of the original source based on information content. This selection can be automated using a classification function induced by a machine learning algorithm. This function classifies sentences into two groups: important or non-important. The important sentences then form the summary. But, the efficiency of this function directly depends on the used training set to induce it. This paper proposes an original way of optimizing this training set by inserting lexemes obtained from ontological knowledge bases. The training set optimized is reinforced by ontological knowledge. An experiment with four machine learning algorithms was made to validate this proposition. The improvement achieved is clearly significant for each of these algorithms.