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title

Text Summarization using Restricted Boltzmann Machine: Unsupervised Deep Learning Approach

Author(s):

Ashwini Ambekar

Keywords:

Stemming, Stop-word removal, Part of Speech Tagging, Title Similarity, Sentence Matrix, Feature Vector Extraction, Inverse sentence frequency, Term weight, Positional feature, Restricted Boltzmann Machine (RBM), Sentence score.

Abstract

Amount of information available on the internet is increasing day by day. A lot of time is required by the user to go through this information or documents. It is difficult for humans to manually summarize the information in these documents. Here, automatic text summarization comes in use. Text summarization is a method of automatically generating a compressed version of the original document. Based on this summary, the user can decide whether or not the document is worth reading and is relevant to his or her topic instead of going through a whole bunch of documents. In this paper, a generalized and query-oriented summary generation method for a single document is proposed.

Other Details

Paper ID: IJSARTV
Published in: Volume : 4, Issue : 6
Publication Date: 6/1/2018

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