Dependency Grammar Based Feature Extraction for Text Summarization
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ABSTRACT:
Recently, there has been a lot of significant research in automatic text summarization. Many researchers use feature-based techniques. Feature based techniques most of the times are based on the soft computing. So they can add only the stastical information. These techniques have shown the efficient results. They fail to add the semantic information in the summary. Here we present an approach to improve the summarization systems. We aim to achieve this target with the use of dependency grammar (DG). This is not been widely applied for text summarization due to its difficulty of handling it in summarization process. At first the input document is subjected for the sentence segmentation. The segmented sentence is then given to the POS tagger, which will be based on DG, for extracting the syntactic structure of the sentence. Every keyword of the sentence is tagged by the POS tagger that is used to extract the syntactic structure of the sentence. The dependency grammar then constructs the syntactic structure trees. Then the neural network is trained based on the syntactic structure of sentences. Finally, the neural network is used with weighted average to find the sentence score .The sentences with high sentence score will be added in summary. The experimentation is carried out using DUC 2002 dataset.
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