Document Summarization using TextRank and Semantic Network

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Ahmad Ashari 1,* Mardhani Riasetiawan 1

1. Department of Computer Science and Electronics, Faculty of Mathematics and Natural Sciences, Universitas Gadjah Mada

* Corresponding author.


Received: 16 Mar. 2017 / Revised: 1 May 2017 / Accepted: 8 Jun. 2017 / Published: 8 Nov. 2017

Index Terms

TextRank, Semantic Network, Document Summarization, Rouge-N, F-Score


The research has implemented document summarizing system uses TextRank algorithms and Semantic Networks and Corpus Statistics. The use of TextRank allows extraction of the main phrases of a document that used as a sentence in the summary output. The TextRank consists of several processes, namely tokenization sentence, the establishment of a graph, the edge value calculation algorithms using Semantic Networks and Corpus Statistics, vertex value calculation, sorting vertex value, and the creation of a summary. Testing has done by calculating the recall, precision, and F-Score of the summary using methods ROUGE-N to measure the quality of the system output. The quality of the summaries influenced by the style of writing, the selection of words and symbols in the document, as well as the length of the summary output of the system. The largest value of the F-Score is 10% of the length ta of the document with the F-Score 0.1635 and 150 words with the F-Score 0.1623.

Cite This Paper

Ahmad Ashari, Mardhani Riasetiawan, "Document Summarization using TextRank and Semantic Network", International Journal of Intelligent Systems and Applications(IJISA), Vol.9, No.11, pp.26-33, 2017. DOI:10.5815/ijisa.2017.11.04


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