Extractive Myanmar News Summarization Using Centroid Based Word Embedding

Lwin, Soe Soe; Nwet, Khin Thandar

doi:10.1109/aitc.2019.8921386

Cited by 6 publications

(3 citation statements)

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“…Lwin and Nwet proposed using fastText sentence embeddings in conjunction with centroid based model for the summarization of Myanmar news articles. The positives of using fastText is that it presents vector representations of the words that are absent from a dictionary [19]. Nitsche and Halbritter presented comparative results of the study of various neural network constructs in performing text classification on a German language dataset.…”

Section: Related Workmentioning

confidence: 99%

Automatic text summarization of konkani texts using pre-trained word embeddings and deep learning

D’Silva

Sharma

2022

IJECE

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<span lang="EN-US">Automatic text summarization has gained immense popularity in research. Previously, several methods have been explored for obtaining effective text summarization outcomes. However, most of the work pertains to the most popular languages spoken in the world. Through this paper, we explore the area of extractive automatic text summarization using deep learning approach and apply it to Konkani language, which is a low-resource language as there are limited resources, such as data, tools, speakers and/or experts in Konkani. In the proposed technique, Facebook’s fastText <br /> pre-trained word embeddings are used to get a vector representation for sentences. Thereafter, deep multi-layer perceptron technique is employed, as a supervised binary classification task for auto-generating summaries using the feature vectors. Using pre-trained fastText word embeddings eliminated the requirement of a large training set and reduced training time. The system generated summaries were evaluated against the ‘gold-standard’ human generated summaries with recall-oriented understudy for gisting evaluation (ROUGE) toolkit. The results thus obtained showed that performance of the proposed system matched closely to the performance of the human annotators in generating summaries.</span>

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Section: Related Workmentioning

confidence: 99%

Automatic text summarization of konkani texts using pre-trained word embeddings and deep learning

D’Silva

Sharma

2022

IJECE

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“…For example, it's possible to apply combinations such as: average, sum, or centroid to obtain a vector that reflect the representation for a sequence of words. We can find an example of centroid usage with Lwin and Nwet (2019) for news summarization extraction or a centroid vector weighted by IDF (Inverse Document Frequency) (Arora, Liang and Ma, 2017).…”

Section: Related Workmentioning

confidence: 99%

BL.Research at SemEval-2022 Task 1: Deep networks for Reverse Dictionary using embeddings and LSTM autoencoders

Bendahman¹,

Breton²,

Nicolaieff³

et al. 2022

Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)

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This paper describes our two deep learning systems that competed at SemEval-2022 Task 1 "CODWOE: Comparing Dictionaries and WOrd Embeddings". We participated in the subtask for the reverse dictionary which consists in generating vectors from glosses. We use sequential models that integrate several neural networks, starting from Embeddings networks until the use of Dense networks, Bidirectional Long Short-Term Memory (BiLSTM) networks and LSTM networks. All glosses have been preprocessed in order to consider the best representation form of the meanings for all words that appears. We achieved very competitive results in reverse dictionary with a second position in English and French languages when using contextualized embeddings, and the same position for English, French and Spanish languages when using char embeddings. Our source code can be found at GitHub 1 .

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“…Sentences and documents are represented in some feature vector space. Centroid can be defined as the whole document's vector [15]. Summary sentences are selected by selecting sentences which have vectors similar to centroid vector.…”

Section: Design Of Proposed Systemmentioning

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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Extractive Myanmar News Summarization Using Centroid Based Word Embedding

Cited by 6 publications

References 4 publications

Automatic text summarization of konkani texts using pre-trained word embeddings and deep learning

Automatic text summarization of konkani texts using pre-trained word embeddings and deep learning

BL.Research at SemEval-2022 Task 1: Deep networks for Reverse Dictionary using embeddings and LSTM autoencoders

Myanmar news summarization using different word representations

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