Abstract
In recent years, people are seeking for a solution to improve text summarization for Thai language. Although several solutions such as PageRank, Graph Rank, Latent Semantic Analysis (LSA) models, etc., have been proposed, research results in Thai text summarization were restricted due to limited corpus in Thai language with complex grammar. This paper applied a text summarization system for Thai travel news based on keyword scored in Thai language by extracting the most relevant sentences from the original document. We compared LSA and Non-negative Matrix Factorization (NMF) to find the algorithm that is suitable with Thai travel news. The suitable compression rates for Generic Sentence Relevance score (GRS) and K-means clustering were also evaluated. From these experiments, we concluded that keyword scored calculation by LSA with sentence selection by GRS is the best algorithm for summarizing Thai Travel News, compared with human with the best compression rate of 20%.
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