Connectionist Approach to Generic Text Summarization
Abstract:As the enormous amount of on-line text grows on the World-Wide Web, the development of methods for automatically summarizing this text becomes more important. The primary goal of this research is to create an efficient tool that is able to summarize large documents automatically. We propose an Evolving connectionist System that is adaptive, incremental learning and knowledge representation system that evolves its structure and functionality. In this paper, we propose a novel approach for Part of Speech disambiguation using a recurrent neural network, a paradigm capable of dealing with sequential data. We observed that connectionist approach to text summarization has a natural way of learning grammatical structures through experience. Experimental results show that our approach achieves acceptable performance.
Keywords: Evolving Systems, artificial neural networks (ANN), Computational Intelligence (CI), Connectionist Text Summarizer ECTS (ECTS), Evolving Connectionist systems, Fuzzy systems (FS), Part of Speech (POS) disambiguation
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1056248Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1211
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