research product . 2016

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

Christian, Hans; Agus, Mikhael Pramodana; Suhartono, Derwin;
Open Access English
  • Published: 01 Jan 2016
  • Country: Indonesia
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 (TermFrequency-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.
ACM Computing Classification System: ComputingMethodologies_DOCUMENTANDTEXTPROCESSING
free text keywords: Indonesia, TF-IDF, natural language processing, automatic text summarization
Related Organizations
  • Digital Humanities and Cultural Heritage
Download from
Open Access
Providers: Neliti
2 research outcomes, page 1 of 1
Any information missing or wrong?Report an Issue