An EDU-Based Approach for Thai Multi-Document Summarization and Its Application

Ketui, Nongnuch; Theeramunkong, Thanaruk; Onsuwan, Chutamanee

doi:10.1145/2641567

Cited by 5 publications

(6 citation statements)

References 22 publications

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“…(1) Low Accuracy of Language Processing Tools. The aforementioned unique characteristics of Thai such as, Thai text does not contain any word/sentence boundary, makes it harder for Thai language processing tools such as TLTK 2 to yield a high accuracy [5,6,16,18]. Consequently, the stylometric features extraction process for Thai is noisier in comparison to English.…”

Section: Limitations Of Existing Studymentioning

confidence: 99%

“…More than 90 million people speak Kra-dai languages and Thai is the most widely spoken Kra-dai language. However, most of the existing authorship identifcation solutions are designed for English [15,16,18]. These solutions are not directly applicable to Thai due to linguistic diferences between English and Thai.…”

Section: Introductionmentioning

confidence: 99%

“…These solutions are not directly applicable to Thai due to linguistic diferences between English and Thai. For example, unlike English [15,16,18], (i) Thai has 44 consonants and 4 tone marks; (ii) Thai has 18 vowel symbols that create many compound vowels, and few special symbols; (iii) Thai text samples do not have word/sentence boundaries; and (iv) the frst person pronounce in Thai can be gender-specifc and gender-neutral (i.e. men tends to use the former and women tends to use the later) [18].…”

Section: Introductionmentioning

confidence: 99%

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StyloThai:

Sarwar

Porthaveepong

Rutherford

et al. 2020

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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Authorship identification helps to identify the true author of a given anonymous document from a set of candidate authors. The applications of this task can be found in several domains, such as law enforcement agencies and information retrieval. These application domains are not limited to a specific language, community, or ethnicity. However, most of the existing solutions are designed for English, and a little attention has been paid to Thai. These existing solutions are not directly applicable to Thai due to the linguistic differences between these two languages. Moreover, the existing solution designed for Thai is unable to (i) handle outliers in the dataset, (ii) scale when the size of the candidate authors set increases, and (iii) perform well when the number of writing samples for each candidate author is low. We identify a stylometric feature space for the Thai authorship identification task. Based on our feature space, we present an authorship identification solution that uses the probabilistic k nearest neighbors classifier by transforming each document into a collection of point sets. Specifically, this document transformation allows us to (i) use set distance measures associated with an outlier handling mechanism, (ii) capture stylistic variations within a document, and (iii) produce multiple predictions for a query document. We create a new Thai authorship identification corpus containing 547 documents from 200 authors, which is significantly larger than the corpus used by the existing study (an increase of 32 folds in terms of the number of candidate authors). The experimental results show that our solution can overcome the limitations of the existing solution and outperforms all competitors with an accuracy level of 91.02%. Moreover, we investigate the effectiveness of each stylometric features category with the help of an ablation study. We found that combining all categories of the stylometric features outperforms the other combinations. Finally, we cross compare the feature spaces and classification methods of all solutions. We found that (i) our solution can scale as the number of candidate authors increases, (ii) our method outperforms all the competitors, and (iii) our feature space provides better performance than the feature space used by the existing study.

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Section: Limitations Of Existing Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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StyloThai:

Sarwar

Porthaveepong

Rutherford

et al. 2020

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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“…Thus forming summary based on frequency of words related to topic has found suitable application in several area [18] [19] of text analysis. A typical approach to generate summary is to identify important or keywords, which is usually carried out by eliminating stop words such as is, are, a, the, then, etc.…”

Section: Text Summarization With Pronoun Frequencymentioning

confidence: 99%

Method to Generate Text Summary by Accounting Pronoun Frequency for Keywords Weightage Computation

2017

IJCCIE

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“…The extractive summarization methods discussed as in [19]- [21] are intend to choose words, sentences and phrases from the given text to obtain the summary. Forming summary based on frequency of words related to the topic has found suitable application in several area [22], [23] of text analysis. It is observed that in a given document the words that are occurring more frequently indicates the subject on which the text is pivoted.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis, Visualization and Classification of Summarized News Articles: A Novel Approach

Urologin¹

2018

ijacsa

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Due to advancement in technology, enormous amount of data is generated every day. One of the main challenges of large amount of data is user overloaded with huge volume of data. Hence effective methods are highly required to help user to comprehend large amount of data. This research work proposes effective methods to extract and represent the data. The summarization is applicable to obtain a brief overview of the text and sentiment analysis can obtain emotions expressed in the text computationally. The combined text summarization and sentiment analysis is proposed on BBC news articles. A pronoun replacement based text summarization method is developed and VADER sentiment analyzer is used to determine sentiment information. The 3-D visualization schemes have been provided to represent the sentiment information. The sentiment analysis and classification are performed on original BBC news articles as well as on summarized articles using classifiers, such as Logistic Regression, Random Forest and Adaboost. On original news articles highest classification rate of 84.93%, using summarization of ratio 25%, 50% and 75% highest classification rates of 78.73%, 83.06% and 83.23%, respectively are observed.

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An EDU-Based Approach for Thai Multi-Document Summarization and Its Application

Cited by 5 publications

References 22 publications

StyloThai:

StyloThai:

Method to Generate Text Summary by Accounting Pronoun Frequency for Keywords Weightage Computation

Sentiment Analysis, Visualization and Classification of Summarized News Articles: A Novel Approach

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