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A New Metric of Validation for Automatic Text Summarization by Extraction

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  • Ahmed Chaouki Lokbani

    (Department of Computer Science, Dr. Tahar Moulay University of Saida, Saida, Algeria)

Abstract

In this article, the author proposes a new metric of evaluation for automatic summaries of texts. In this case, the adaptation of the F-measure that generates a hybrid method of evaluating an automatic summary at the same time as both extrinsic and intrinsic. The article starts by studying the feasibility of adaptation of the F-measure for the evaluation of automatic summarization. After that, the author defines how to calculate the F-measure for a candidate summary. Text is presented with a term vector which can be either a word or a phrase, with a binary-weighted or occurrence. Finally, to determine to the exactitude of evaluation of the F-measure for automatic summarization by extraction calculates correlation with the ROUGE Evaluation.

Suggested Citation

  • Ahmed Chaouki Lokbani, 2017. "A New Metric of Validation for Automatic Text Summarization by Extraction," International Journal of Strategic Information Technology and Applications (IJSITA), IGI Global, vol. 8(3), pages 20-40, July.
    • Handle: RePEc:igg:jsita0:v:8:y:2017:i:3:p:20-40
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