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Statistical and Analytical Study of Guided Abstractive Text Summarization


Affiliations
1 Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Kakinada, India
2 Department of Computer Science and Engineering, M S Ramaiah Institute of Technology, Bengaluru, India
3 JNTUA College of Engineering, Jawaharlal Nehru Technological University, Anantapur, India
 

The process of creating condensed version of given text document by collecting only the important information in it is called abstractive summarization. This involves structuring the information into sentences which are simple and easy to understand. This article presents the analytical study of the process that generates abstractive summary using unified model with attribute based information extraction (IE) rules and class based templates. Classification of the document into several categories is achieved by term frequency/ inverse document frequency (TF/IDF) rules. To generate the information intensive summaries, we use templates for sentence generation. The IE rules are designed to address the complexities involved in Indian regional languages. This paper statistically analyzes the adaptation of the methodology over multiple Indian languages and many document categories. Comparisons between abstractive and extractive summaries are also presented.

Keywords

Abstractive and Extractive Text Summarizations, Information Extraction, Language Parsing and Understanding, Template Selection, Template-Based Generation.
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  • Statistical and Analytical Study of Guided Abstractive Text Summarization

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Authors

Jagadish S. Kallimani
Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Kakinada, India
K. G. Srinivasa
Department of Computer Science and Engineering, M S Ramaiah Institute of Technology, Bengaluru, India
B. Eswara Reddy
JNTUA College of Engineering, Jawaharlal Nehru Technological University, Anantapur, India

Abstract


The process of creating condensed version of given text document by collecting only the important information in it is called abstractive summarization. This involves structuring the information into sentences which are simple and easy to understand. This article presents the analytical study of the process that generates abstractive summary using unified model with attribute based information extraction (IE) rules and class based templates. Classification of the document into several categories is achieved by term frequency/ inverse document frequency (TF/IDF) rules. To generate the information intensive summaries, we use templates for sentence generation. The IE rules are designed to address the complexities involved in Indian regional languages. This paper statistically analyzes the adaptation of the methodology over multiple Indian languages and many document categories. Comparisons between abstractive and extractive summaries are also presented.

Keywords


Abstractive and Extractive Text Summarizations, Information Extraction, Language Parsing and Understanding, Template Selection, Template-Based Generation.

References





DOI: https://doi.org/10.18520/cs%2Fv110%2Fi1%2F69-72