Comparison of Document Index Graph Using TextRank and HITS Weighting Method in Automatic Text Summarization

Hadyan, Fadhlil; Shaufiah,; Bijaksana, Moch Arif

doi:10.1088/1742-6596/801/1/012076

Cited by 2 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The next stage is to form summary sentences. This stage was designed using the seq2seq RNN method as in Wang's study [15]. In Wang's research, it can be seen that with this method, the summary results are better than the usual RNN.…”

Section: Feature Extractionmentioning

confidence: 99%

“…Selection of the core sentence based on the relationship between the text title and the sentence and the relationship between the sentences. However, according to Hadyan's [15] research, which performed extractive summarization using Pagerank in Indonesian text, the Fmeasure obtained was not very good. So in this study, the stages of making extractive summaries will be carried out using the Support Vector Machine method, which can be seen in research [3] that can produce fairly good accuracy.…”

Section: Introductionmentioning

confidence: 98%

See 1 more Smart Citation

The Design of Automatic Summarization of Indonesian Texts Using a Hybrid Approach

Dewi

Widiastuti

2022

J. teknol. inf. pendidik.

View full text Add to dashboard Cite

This study aims to design a model for automatic text summarization in Indonesian. Automatic text summarization is a system that reduces the number of sentences without losing important information in the document. There are three approaches in making automatic text summarization, namely: extractive, abstractive, and hybrid. The extractive approach is to select a core sentence without changing it to a new sentence. The abstractive approach is to construct new sentences that describe the contents of the document. Meanwhile, the hybrid approach designed in this study is a combination of extractive and abstractive approaches. By designing automatic text summarization in Indonesian with a hybrid approach, it is hoped that the results issued by the system will be more like man-made summaries and have higher readability. The design made is specifically for the input of a document. The stages of automatic text summarization are divided into two, namely preprocessing and process. In the Preprocessing stage, sentence separation, tokenization, coreference resolution, stop words removal, feature extraction are carried out. There are two stages of making the summary, namely the extraction stage, to select important sentences and the abstraction stage, to select words and arrange them into summary sentences. Further research can be carried out for input of longer documents and the input is in the form of multi documents

show abstract

Section: Feature Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%