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Sargule, Swati
- Hindi Language Document Summarization Using Context Based Indexing Model
Authors
1 Department of Computer Engineering, Dr. D. Y. Patil School of Engineering and Technology, Lohegaon, Pune, IN
2 Department of Computer Engineering, Dr. D. Y. Patil School of Engineering and Technology, Charoli, B. K. Via, Lohegaon, Pune, Maharashtra, IN
Source
Data Mining and Knowledge Engineering, Vol 8, No 1 (2016), Pagination: 1-6Abstract
Hindi Document Summarization (DS) is an Information Retrieval (IR) process in which summery of document is extracted to provide overview of that document. Existing document summarization models generally use the similarity among sentences in the original document to extract the maximum significant sentences. The documents along with the sentences are generally indexed using standard term indexing computation techniques, which do not take into account the context related to document. Thus, the similarity values of sentence are independent of the context. In this paper, a context sensitive document indexing model is propose which based on the Bernoulli model of randomness for Hindi text document. The Bernoulli model has been used to check the probability of the co-occurrences of two terms in a large set of documents.Keywords
Document Summarization, Lexical Association, Context Indexing.
- Methodology of Context Centered Term Indexing Style Intended For Hindi Language Document Summarization
Authors
Source
Software Engineering, Vol 8, No 5 (2016), Pagination: 125-130Abstract
Document outline is beneficial since it will provide the summary of the initial document in an exceedingly shorter amount of your time. Document summarization largely uses the similarity between sentences within the document to extract the foremost salient sentences. The documents moreover because the sentences are indexed using ancient term indexing measures, that don't take the context into thought. Therefore, the sentence similarity values stay freelance of the context. The planned system consists of a context sensitive document compartmentalization model supported the Bernoulli model of randomness. The Bernoulli model of randomness has been accustomed notice the probability of the concurrences of 2 terms in passing big corpus. A replacement approach practice the lexical association between terms to relinquish a context sensitive weight to the document terms has been planned. The following compartmentalization weights are accustomed reason the sentence similarity matrix. The planned sentence similarity live has been used with the baseline graph-based ranking models for sentence extraction.