Publication Type : Conference Paper
Publisher : 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI)
Source : 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), IEEE, Udupi, India (2017)
Url : https://ieeexplore.ieee.org/document/8125914
ISBN : 9781509063673
Keywords : Batch-OMP, Data mining, Dictionaries, Dictionary learning, document handling, document summarization, Encoding, impressionistic human intervention mediation, Iterative methods, learning (artificial intelligence), Machine learning, Matching pursuit algorithms, multiple documents, OMP, Orthogonal matching pursuit, Semantics, Sentence ordering, Signal processing, Singular value decomposition, Software applications, sparse code, Sparse coding, sparse coding techniques, Sparse matrices, SVD
Campus : Amritapuri
School : Center for Allied Health Sciences, Department of Computer Science and Engineering, School of Engineering
Department : Computer Science and Engineering, Computer Science
Year : 2017
Abstract : Document summarization is a strategy, intended to extract information from multiple documents, deliberating the same subject. Many software applications handle document summarization, helping people grab the main thought, from a collection of documents, within a short time. Automatic summaries present information algorithmically extracted from multiple sources, without any impressionistic human intervention mediation. Experiments have resulted in ingenious algorithms, surmount the task of creating a short and salient summary. One such technique suggested in this paper is Dictionary Learning. This paper focuses on Document summarization, using dictionary learning and sparse coding techniques, considering the ordering of sentences and redundancy of documents. We use Singular Value Decomposition(SVD) for dictionary learning and Orthogonal Matching Pursuit(OMP) for sparse coding. The application of SVD augments the semantics of the generated summary. The order of sparsity in the final sparse code is used in ordering the sentences in the final summary. Verification of our proposed methodology have shown 75% precision.
Cite this Research Publication : Remya Rajesh and Aswathy, N., “Document Summarization Using Dictionary Learning”, in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Udupi, India, 2017
