Myanmar news summarization using different word representations

There is enormous amount information available in different forms of sources and genres. In order to extract useful information from a massiveamount of data, automatic mechanism is required. The text summarization systems assist with content reduction keeping the important information and filtering the non-important parts of the text. Good document representation isreally important in text summarization to get relevant information. Bag-ofwords cannot give word similarity on syntactic and semantic relationshipWord embedding can give good document representation to capture andencode the semantic relation between words. Therefore, centroid based on word embedding representation is employed in this paper. Myanmar news summarization based on different word embedding is proposed. In this paperMyanmar local and international news are summarized using centroid-basedword embedding summarizer using the effectiveness of word representationapproach, word embedding. Experiments were done on Myanmar local andinternational news dataset using different word embedding models and theresults are compared with performance of bag-of-words summarization.Centroid summarization using word embedding performs comprehensivelybetter than centroid summarization using bag-of-words.