A New Metric of Validation for Automatic Text Summarization by Extraction

Author:

Lokbani Ahmed Chaouki1

Affiliation:

1. 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.

Publisher

IGI Global

Subject

General Materials Science

Reference22 articles.

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4. Keyphrase extraction for N -best Reranking in Multi- Sentence Compression.;F.Boudin;Proceedings of the North American Chapter of the Association for Computational Linguistics (NAACL),2013

5. Cao, Z., Wei, F., Dong, L., Li, S., & Zhou, M. (2015, February). Ranking with Recursive Neural Networks and Its Application to Multi-Document Summarization. In AAAI (pp. 2153-2159).

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