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- W2039696522 abstract "World Wide Web is the largest source of information. Huge amount of data is present on the Web. There has been a great amount of work on query‐independent summarization of documents. However, due to the success of Web search engines query‐specific document summarization (query result snippets) has become an important problem. In this paper a method to create query specific summaries by identifying the most query‐relevant fragments and combining them using the semantic associations within the document is discussed. In particular, first a structure is added to the documents in the preprocessing stage and converts them to document graphs. The present research work focuses on analytical study of different document clustering and summarization techniques currently the most research is focused on Query‐Independent summarization. The main aim of this research work is to combine the both approaches of document clustering and query dependent summarization. This mainly includes applying different clustering algorithms on a text document. Create a weighted document graph of the resulting graph based on the keywords. And obtain the document graph to get the summary of the document. The performance of the summary using different clustering techniques will be analyzed and the optimal approach will be suggested." @default.
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- W2039696522 date "2010-01-01" @default.
- W2039696522 modified "2023-10-16" @default.
- W2039696522 title "Summarization of Text Document Using Query Dependent Parsing Techniques" @default.
- W2039696522 doi "https://doi.org/10.1063/1.3526213" @default.
- W2039696522 hasPublicationYear "2010" @default.
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