Abstract
Finding the relevant information from a given document is one of the major problems in today’s information world. Text summarization is the process of reducing the size of a text document in order to generate a summary that contains the salient points of the original document. The paper proposes an extractive text summarization framework which uses Deep Neural Network (DNN) to obtain a representative subset of the input document by selecting those sentences which contribute the most to the entire content of the document. The major advantage of the proposed framework is its ability to discover the intrinsic semantic space that enables the extraction of semantically relevant sentences. Hence, the information coverage can be increased without contributing to the redundancy in the summary. The qualitative analysis in the experiments on the datasets of Multiling-2015 showed that the proposed system produces summaries of good virtue.
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Arjun, K., Hariharan, M., Anand, P., Pradeep, V., Raj, R., Mohan, A. (2018). Extractive Text Summarization Using Deep Auto-encoders. In: Sa, P., Bakshi, S., Hatzilygeroudis, I., Sahoo, M. (eds) Recent Findings in Intelligent Computing Techniques . Advances in Intelligent Systems and Computing, vol 709. Springer, Singapore. https://doi.org/10.1007/978-981-10-8633-5_18
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DOI: https://doi.org/10.1007/978-981-10-8633-5_18
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