Hybrid methodologies for summarisation of Kannada language text documents
by R. Jayashree; K. Srikanta Murthy; Basavaraj S. Anami
International Journal of Knowledge Engineering and Data Mining (IJKEDM), Vol. 3, No. 1, 2014

Abstract: The problem of information explosion is becoming a serious concern. In this regard, any new methodology developed to solve the issues related to information retrieval (a.k.a. Information Retrieval or IR) draws wide attention. Text summarisation is a predominant field of NLP which may provide promising solution to the issues stated earlier. Text summarisation or text document summarisation provides a quick and concise meaning of the document without even reading the whole document. In this work, we have developed hybrid methodologies for providing summary of a given document in the Kannada language. The approach is new as we have used combination of feature selection methods as a pre-processing step for summarisation. In this work, we have devised four different methodologies for text document summarisation, which focus on text extraction, which is an open approach as stated earlier: (a) summarisation based on keywords, (b) summarisation based on sentence ranking, (c) summarisation based on Jaccards' similarity score and (d) summarisation based on neural network approach.

Online publication date: Mon, 08-Dec-2014

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