Single Document Automatic Text Summarization using Term Frequency-Inverse Document Frequency (TF-IDF)

Authors

  • Hans Christian Bina Nusantara University
  • Mikhael Pramodana Agus Bina Nusantara University
  • Derwin Suhartono Bina Nusantara University

DOI:

https://doi.org/10.21512/comtech.v7i4.3746

Keywords:

automatic text summarization, natural language processing, TF-IDF

Abstract

The increasing availability of online information has triggered an intensive research in the area of automatic text summarization within the Natural Language Processing (NLP). Text summarization reduces the text by removing the less useful information which helps the reader to find the required information quickly. There are many kinds of algorithms that can be used to summarize the text. One of them is TF-IDF (Term
Frequency-Inverse Document Frequency). This research aimed to produce an automatic text summarizer implemented with TF-IDF algorithm and to compare it with other various online source of automatic text summarizer. To evaluate the summary produced from each summarizer, The F-Measure as the standard comparison value had been used. The result of this research produces 67% of accuracy with three data samples which are higher compared to the other online summarizers.
Dimensions

Plum Analytics

Author Biographies

Hans Christian, Bina Nusantara University

School of Computer Science

Mikhael Pramodana Agus, Bina Nusantara University

School of Computer Science

Derwin Suhartono, Bina Nusantara University

School of Computer Science

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Published

2016-12-31

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