Abstract
In this paper, we present a new hybrid system for automatic text summarization. First, vector space modelling is used to compute two original metrics of coverage and fidelity. The latter metrics are combined onto a unified Fidelity-Coverage (F-C) score using fuzzy logic theory. Then, a rhetorical analysis is performed on top of sentences having the highest F-C scores in order to achieve coherence. Conducted experiments on the Timeline17 dataset show that the proposed system outperforms state of the art extractive summarization models. Also, generated abstracts generally satisfy the three criteria of a good summary, namely coverage, fidelity and coherence.
Supported by the Canadian Social Sciences and Humanities Research Council.
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Ben Ayed, A., Biskri, I., Meunier, JG. (2019). Automatic Text Summarization: A New Hybrid Model Based on Vector Space Modelling, Fuzzy Logic and Rhetorical Structure Analysis. In: Nguyen, N., Chbeir, R., Exposito, E., Aniorté, P., Trawiński, B. (eds) Computational Collective Intelligence. ICCCI 2019. Lecture Notes in Computer Science(), vol 11684. Springer, Cham. https://doi.org/10.1007/978-3-030-28374-2_3
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