Abs-Sum-Kan: An Abstractive Text Summarization Technique for an Indian Regional Language by Induction of Tagging Rules
Shilpa G V1, Shashi Kumar D R2
1Shilpa G V, Vemana Institute of Technology, Bangalore (Karnataka), India.
2Shashi Kumar D R, Cambridge Institute of Technology, Bangalore (Karnataka), India.
Manuscript received on 22 July 2019 | Revised Manuscript received on 03 August 2019 | Manuscript Published on 10 August 2019 | PP: 1028-1036 | Volume-8 Issue-2S3 July 2019 | Retrieval Number: B11930782S319/2019©BEIESP | DOI: 10.35940/ijrte.B1193.0782S319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper presents a full abstraction for Indian languages, specifically Kannada, in the context of guided summarization. The proposed process generates the abstractive sum-mary by focusing on a unified presentation model with aspect based Information Extrac-tion (IE) rules and scheme based Templates. TF/IDF rules are used for classification into categories. Lexical analysis (like Parts Of Speech tagging and Named Entity Recognition) reduces prolixity, which leads to robust IE rules. Usage of Templates for sentence genera-tion makes the summaries succinct and information intensive. The IE rules are designed to accommodate the complexities of the considered languages. Later, the system aims to produce a guided summary of domain specific documents. An abstraction scheme is a collection of aspects and associated IE rules. Each abstraction scheme is designed based on a theme or subcategory. An extensive statistical and qualitative evaluation of the summaries generated by the system has been conducted and the results are found to be very promising.
Keywords: Abstractive Summary, Information Extraction, Kannada, Template based Generation, Template Selection.
Scope of the Article: Text Mining