Evaluation of Multi Document Summarization Techniques

Neduncheli, R.; Muthucumar, R.; Saranathan, E.

doi:10.3923/rjasci.2012.229.233

Cited by 1 publication

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“…Hence the sentence consisting of this keyword generates higher sentence score than the other and most likely to be selected as a part of the summary. According to Nedunchelian et al (2011), the evaluation process of text summarization is performed by using three parameters which are precision, recall, and f-measure. Table 8, Table 9, and Table 10 represent the performance evaluation of the three different summarizers by using those parameters.…”

Section: Resultsmentioning

confidence: 99%

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

Christian

Agus

Suhartono

2016

ComTech

183

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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.

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Section: Resultsmentioning

confidence: 99%