Lot of work has already been done for automatic text summarization. In this paper we have given a novel statistical approach to summarize the given Odia text. In our approach extraction of relevant sentences is done which can give the actual concept of the input document in a concise form. We rank each sentence in the document by assigning a weight value to each word of the sentence. The sentences are extracted as per their rank, which will lead to a good summary of the given text.

No abstract

The task of retrieving the theme of a document and presenting a shorter form compared to the original text to the user is a challenging assignment. In this article, a hybrid approach to extract knowledge from a text document is presented, in which three key sentence level relationships in association with the Markov clustering algorithm is used to cluster sentences in the document. After clustering, sentences are ranked in each cluster and the highest ranked sentences in each cluster are merged. In the end, to get the final theme of the document, the Gradient boosting technique XGboost is used to compress the newly generated sentence. The DUC-2002 data set is used to evaluate the proposed system and it has been observed that the performance of the proposed system is better than other existing systems.

No abstract

No abstract