May Thu Naing

Work place: Information Techology Supporting and Maintainence Department, University of Computer Studies, Taunggyi, Myanmar



Research Interests: Computer systems and computational processes, Natural Language Processing, Data Mining


May Thu Naing

She is an Assistant Lecturer of Information Techology Supporting and Maintainence Department at University of Computer Studies, Taunggyi, Myanmar. She is a member of Natural Language Processing Project. Her research interests include Data Mining and Natural Language Processing. She is currently working Myanmar to English, Paoah, Shan Translation System Project in Taunggyi, Myanmar. She received B.C.Sc(Hons) degree from Computer University (Mandalay) in 2008, M.C.Sc degree in 2010 and her Ph.D degree in Computer Science from the University of Computer Studies, Mandalay(UCSM), Myanmar in 2016.

Author Articles
Text Summarization System Using Myanmar Verb Frame Resources

By May Thu Naing Aye Thida

DOI:, Pub. Date: 8 Oct. 2018

In today’s era, when the size of information and data is increasing exponentially, there is an upcoming need to create a concise version of the information available. Until now, humans have tried to create “summaries” of the documents. Especially, Myanmar Natural Language Processing does not have computerized text summarization. Therefore, this paper presents a summary generation system that will accept a single document as input in Myanmar. In addition, this work presents analysis on the influence of the semantic roles in summary generation. The proposed text summarization system involves three steps: first, the sentences are parsed using Part of Speech tagger with Myanmar Language Tool Knowledge Resource (ML2KR); secondly, pronouns in the original text are resolved using Myanmar Pronoun Resolution Algorithm (MPAR); thirdly, the sentences are labeled with semantic roles using Myanmar Verb Frame Resource (MVF), finally, extraction of the sentences containing specific semantic roles for the most relevant entities in text. After that, the system abstracts the important information in fewer words from extraction summary from single documents.

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