© Amrita Vishwa Vidyapeetham Amritapuri | Bengaluru | Chennai | Coimbatore | Kochi | Mysuru
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.
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.