Ontology-based Text Summarization for Business News Articles

† In this paper, we compare two methods for article summarization. The first method is mainly based on term-frequency, while the second method is based on ontology. We build an ontology database for analyzing the main topics of the article. After identifying the main topics and determining their relative significance, we rank the paragraphs based on the relevance between main topics and each individual paragraph. Depending on the ranks, we choose desired proportion of paragraphs as summary. Experimental results indicate that both methods offer similar accuracy in their selections of the paragraphs. Keywords Information Retrieval, Natural Language Processing, Ontology Application, Text Summarization

• Type: Conference
• Year: 2003
• Where: CATA
• Authors: Chia-Wei Wu, Chao-Lin Liu
• Keywords: Article Summarization, CATA 2003, CATA 2007, Main Topics, Ontology Database