Summarizing Indonesian text automatically by using sentence scoring and decision tree

Sabuna, Periantu Marhendri; Setyohadi, Djoko Budiyanto

doi:10.1109/icitisee.2017.8285473

Cited by 18 publications

(9 citation statements)

References 12 publications

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“…Machine learning methods are widely used in the summarization process. For example SVM [14], ANN [15], and Decision Tree [16] are various types of machine learning methods used in summarizing.…”

Section: -1-machine Learning-based Approachesmentioning

confidence: 99%

A Survey on Multi-document Summarization and Domain-Oriented Approaches

Afsharizadeh¹,

Ebrhimpour-Komeleh²,

Bagheri³

et al. 2022

jist

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Before the advent of the World Wide Web, lack of information was a problem. But with the advent of the web today, we are faced with an explosive amount of information in every area of search. This extra information is troublesome and prevents a quick and correct decision. This is the problem of information overload. Multi-document summarization is an important solution for this problem by producing a brief summary containing the most important information from a set of documents in a short time. This summary should preserve the main concepts of the documents. When the input documents are related to a specific domain, for example, medicine or law, summarization faces more challenges. Domain-oriented summarization methods use special characteristics related to that domain to generate summaries. This paper introduces the purpose of multi-document summarization systems and discusses domain-oriented approaches. Various methods have been proposed by researchers for multi-document summarization. This survey reviews the categorizations that authors have made on multi-document summarization methods. We also categorize the multi-document summarization methods into six categories: machine learning, clustering, graph, Latent Dirichlet Allocation (LDA), optimization, and deep learning. We review the different methods presented in each of these groups. We also compare the advantages and disadvantages of these groups. We have discussed the standard datasets used in this field, evaluation measures, challenges and recommendations.

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Section: -1-machine Learning-based Approachesmentioning

confidence: 99%

A Survey on Multi-document Summarization and Domain-Oriented Approaches

Afsharizadeh¹,

Ebrhimpour-Komeleh²,

Bagheri³

et al. 2022

jist

View full text Add to dashboard Cite

show abstract

“…Such an approach requires dividing the dataset into training, testing that data for labeling, and categorizing it accordingly. Some of the machine learning approaches are Naïve Bayes [16], neural network [17], decision trees [18] and support vector machine [19]. Many researchers have also investigated optimization approaches.…”

Section: Related Workmentioning

confidence: 99%

Extractive multi document summarization using harmony search algorithm

Ali¹,

Hussein²,

Abass³

et al. 2021

TELKOMNIKA

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The exponential growth of information on the internet makes it troublesome for users to get valuable information. Text summarization is the process to overcome such a problem. An adequate summary must have wide coverage, high diversity, and high readability. In this article, a new method for multidocument summarization has been supposed based on a harmony search algorithm that optimizes the coverage, diversity, and readability. Concerning the benchmark dataset Text Analysis Conference (TAC-2011), the ROUGE package used to measure the effectiveness of the proposed model. The calculated results support the effectiveness of the proposed approach.

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“…Sabuna and Setyohadi [12] proposed eight features in the process summarization. Each sentence is scored based on those eight features.…”

Section: Machine Learning Based Approachesmentioning

confidence: 99%

A Review Paper on Automatic Text Summarization in Indonesia Language

Khotimah¹,

P²,

Andreas³

et al. 2021

IJETAE

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Text summarization is one problem in natural language processing that generates a brief version of the original document. This research took attention for some researchers in this last decade and growing fast, including Indonesia language. This paper aims to recap summarization text research especially in Indonesia language. As usual, this paper discusses two summarization approaches, extractive and abstractive. In fact, the number of research of extractive is more than abstractive. This paper investigates some methods such as Statistical Based Approach, Graph Based Approach, Machine Learning Approach, Fuzzy Logic Approach, Algebraic Approach, and Hybrid Approach. This paper shows some methods details and summarize the results. Keywords— Text summarization, extractive summary, abstractive summary, natural language processing

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Summarizing Indonesian text automatically by using sentence scoring and decision tree

Cited by 18 publications

References 12 publications

A Survey on Multi-document Summarization and Domain-Oriented Approaches

A Survey on Multi-document Summarization and Domain-Oriented Approaches

Extractive multi document summarization using harmony search algorithm

A Review Paper on Automatic Text Summarization in Indonesia Language

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