Indonesian Abstractive Text Summarization Using Bidirectional Gated Recurrent Unit

Adelia, Rike; Suyanto, Suyanto; Wisesty, Untari Novia

doi:10.1016/j.procs.2019.09.017

Cited by 50 publications

(21 citation statements)

References 9 publications

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“…All the webpages and informal documents have keywords or page rank keywords to represent the actual text document's theme. TF-IDF and PRK generally help to assess the contents of relevant text documents [7], [16], [26]. However, for in-depth text assessments, multiple text processing techniques require systematically joined to relate text segments [9], [14].…”

Section: Discussionmentioning

confidence: 99%

“…The principle of this morphology relies on analyzing the end of a word for predicting its morphological features. [26], [37]. The English language MA is based on the use of finite state machines and finite state transducer [23], [24], [31].…”

Section: B Dictionary-based Approachesmentioning

confidence: 99%

“…Almost all topics characterization likelihood, by certain words, has IDF close to 2 [24], [25], [44]. TF-IDF gives maximum value if rare words have many occurrences in the document [26], [29]. This indicator calculation works as below:…”

Section: Text Relevance By Term Frequency-inverse Document Frequenmentioning

confidence: 99%

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Unstructured Text Documents Summarization With Multi-Stage Clustering

et al. 2020

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In natural language processing, text summarization is an important application used to extract desired information by reducing large text. Existing studies use keyword-based algorithms for grouping text, which do not give the documents' actual theme. Our proposed dynamic corpus creation mechanism combines metadata with summarized extracted text. The proposed approach analyzes the mesh of multiple unstructured documents and generates a linked set of multiple weighted nodes by applying multistage Clustering. We have generated adjacency graphs to link the clusters of various collections of documents. This approach comprises of ten steps: pre-processing, making multiple corpuses, first stage clustering, creating sub-corpuses, interlinking sub-corpuses, creating page rank keyword dictionary of each sub-corpus, second stage clustering, path creation among clusters of sub-corpuses, text processing by forward and backward propagation for results generation. The outcome of this technique consists of interlinked subcorpuses through clusters. We have applied our approach to a News dataset, and this interlinked corpus processing follows step by step clustering to search the most relevant parts of the corpus with less cost, time, and improve content detection. We have applied six different metadata processing combinations over multiple text queries to compare results during our experimentation. The comparison results of text satisfaction show that Page-Rank keywords give 38% related text, single-stage Clustering gives 46%, twostage Clustering gives 54%, and the proposed technique gives 67% associated text. Furthermore, this approach covers/searches the relevant data with a range of most to less relevant content. It provides the systematic query-relevant corpus processing mechanism, which automatically selects the most relevant subcorpus through dynamic path selection. We used the SHAP model to evaluate the proposed technique, and our evaluation results proved that the proposed mechanism improved text processing. Moreover, combining text summarization features, shown satisfactory results compared to the summaries generated by general models of abstractive & extractive summarization.

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Section: Discussionmentioning

confidence: 99%

Section: B Dictionary-based Approachesmentioning

confidence: 99%

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Unstructured Text Documents Summarization With Multi-Stage Clustering

et al. 2020

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“…Adelia et al [1] used Bidirectional GRU to consider the score between abstract and original text. This proposed method uses abstractive text summarization which generates new summary text based on important points/context.…”

Section: Machine Learning Based Approachesmentioning

confidence: 99%

“…Abstractive text summarization is a technique to construe the original document to new document which can be different literally but the same in meaning in the context of the sentence. Adelia et al [1] developed an abstractive summarization generate summary from Indonesian language journal documents that will be assigned as abstract. Extractive text summarization method generates a summary by taking some sentences from the original documents.…”

Section: Introductionmentioning

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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A topic modeling‐based bibliometric exploration of automatic summarization research

Chen,

Xie,

Tao

et al. 2024

WIREs Data Min & Knowl

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The surge in text data has driven extensive research into developing diverse automatic summarization approaches to effectively handle vast textual information. There are several reviews on this topic, yet no large‐scale analysis based on quantitative approaches has been conducted. To provide a comprehensive overview of the field, this study conducted a bibliometric analysis of 3108 papers published from 2010 to 2022, focusing on automatic summarization research regarding topics and trends, top sources, countries/regions, institutions, researchers, and scientific collaborations. We have identified the following trends. First, the number of papers has experienced 65% growth, with the majority being published in computer science conferences. Second, Asian countries and institutions, notably China and India, actively engage in this field and demonstrate a strong inclination toward inter‐regional international collaboration, contributing to more than 24% and 20% of the output, respectively. Third, researchers show a high level of interest in multihead and attention mechanisms, graph‐based semantic analysis, and topic modeling and clustering techniques, with each topic having a prevalence of over 10%. Finally, scholars have been increasingly interested in self‐supervised and zero/few‐shot learning, multihead and attention mechanisms, and temporal analysis and event detection. This study is valuable when it comes to enhancing scholars' and practitioners' understanding of the current hotspots and future directions in automatic summarization.This article is categorized under: Algorithmic Development > Text Mining

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Indonesian Abstractive Text Summarization Using Bidirectional Gated Recurrent Unit

Cited by 50 publications

References 9 publications

Unstructured Text Documents Summarization With Multi-Stage Clustering

Unstructured Text Documents Summarization With Multi-Stage Clustering

A Review Paper on Automatic Text Summarization in Indonesia Language

A topic modeling‐based bibliometric exploration of automatic summarization research

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