Text Summarization Based on Classification Using ANFIS

The information overload faced by today’s society has created a big challenge for people who want to look for relevant information from the internet. There are a lot of online documents available and digesting such large texts collection is not an easy task. Hence, automatic text summarization is required to automate the process of summarizing text by extracting only the salient information from the documents. In this paper, we propose a text summarization model based on classification using Adaptive Neuro-Fuzzy Inference System (ANFIS). The model can learn to filter high quality summary sentences. We then compare the performance of our proposed model with the existing approaches which are based on neural network and fuzzy logic techniques. ANFIS was able to alleviate the limitations in the existing approaches and the experimental finding of this study shows that the proposed model yields better results in terms of precision, recall and F-measure on the Document Understanding Conference (DUC) data corpus.

[1]  Ong Sing Goh,et al.  A Review on Automatic Text Summarization Approaches , 2016, J. Comput. Sci..

[2]  C. Loganathan,et al.  Investigations on Hybrid Learning in ANFIS , 2014 .

[3]  Naomie Salim,et al.  Genetic semantic graph approach for multi-document abstractive summarization , 2015, 2015 Fifth International Conference on Digital Information Processing and Communications (ICDIPC).

[4]  Rucha S. Dixit,et al.  Improvement of Text Summarization using Fuzzy Logic Based Method , 2012 .

[5]  Saroj Kaushik,et al.  Automatic Text Summarization , 2008 .

[6]  Naomie Salim,et al.  Multi document summarization based on news components using fuzzy cross-document relations , 2014, Appl. Soft Comput..

[7]  K. G. Srinivasagan,et al.  Multi-Document and Multi-Lingual Summarization using Neural Networks , 2012 .

[8]  Y. M. Arikat Subtractive Neuro-Fuzzy modeling techniques applied to short essay auto- grading problem , 2012, 2012 6th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT).

[9]  David W. Conrath,et al.  Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy , 1997, ROCLING/IJCLCLP.

[10]  Eng Aik Lim,et al.  A Study of Neuro-fuzzy System in Approximation-based Problems , 2008 .

[11]  Åsvald Lima,et al.  Geometry of spaces of compact operators , 2008 .

[12]  S. A. Babar,et al.  Improving Performance of Text Summarization , 2015 .

[13]  Naomie Salim,et al.  Fuzzy Logic Based Method for Improving Text Summarization , 2009, ArXiv.