Hybrid methodologies for summarisation of Kannada language text documents. Issue 1 (9th December 2014)

• Record Type: Journal Article
• Authors: Jayashree, R.; Murthy, K. Srikanta; Anami, Basavaraj S.
• Is Part Of: International journal of knowledge engineering and data mining. Volume 3:Issue 1(2014)
• Page Start: 82
• Page End: 114
• Publication Date: 2014-12-09
• Subjects: document summaries -- text extraction -- feature selection -- keywords -- sentence ranking -- similarity scores -- GSS -- IDF -- chi–square -- neural networks -- Jaccard similarity -- thematic words -- summarisation -- Kannada language text -- information retrieval; Knowledge representation (Information theory) -- Periodicals; Data mining -- Periodicals; 006.305
• Languages: English
• ISSNs: 1755-2087

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.