Extractive Summarization for Myanmar Language

Lwin, Soe Soe; Nwet, Khin Thandar

doi:10.1109/isai-nlp.2018.8692976

Cited by 4 publications

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“…Abstractive summarization uses new words to form the summary to describe the main content. For the first step, there are two types to represent the input text: topic representation approaches (centroid based method, latent semantic analysis, Discourse based method, Bayesian topic models) [2][3][4] and indicator representation approaches (graph-based method, machine learning) [5][6]. When the intermediate representation is generated, an importance score is assigned to each sentence in second step.…”

Section: Introductionmentioning

confidence: 99%

Myanmar news summarization using different word representations

Lwin¹,

Nwet

2021

IJECE

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There is enormous amount information available in different forms of sources and genres. In order to extract useful information from a massive amount 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 is really important in text summarization to get relevant information. Bag-of-words cannot give word similarity on syntactic and semantic relationship. Word embedding can give good document representation to capture and encode 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 paper, Myanmar local and international news are summarized using centroid-based word embedding summarizer using the effectiveness of word representation approach, word embedding. Experiments were done on Myanmar local and international news dataset using different word embedding models and the results are compared with performance of bag-of-words summarization. Centroid summarization using word embedding performs comprehensively better than centroid summarization using bag-of-words.

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