No abstract

Abstractategory classification, for news, is a multi-label text classification problem. The goal is to assign one or more categories to a news article. A standard technique in multi-label text classification is to use a set of binary classifiers. For each category, a classifier is used to give a "yes" or "no" answer on if the category should be assigned to a text. Some of the standard algorithms for text classification that are used for binary classifiers include Naive Bayesian Classifiers, Support Vector Machines, artificial neural networks etc. In this distinctive bag of words have been used as feature set based on high frequency word tokens found in individual category of news. The algorithm presented in this work is based on a keyword extraction algorithm that is capable of dealing with English language in which different news categories i.e. Business, entertainment, politics, sports etc. has been considered. Intra-class news classification has been carried out in which Cricket and Football in sports category has been selected to verify the performance of the algorithm. Experimental results shows high classification rate in describing category of a news document.

The reduced text of a document is the collection of sentences that contains the important sentences containing keywords of the document. The authentic keywords extraction is the primary target for any text reduction algorithm. The presented survey shows the primary algorithm used for document summarization based on keywords. Also, the work presents a novel approach for keywords identification and in turn text reduction based on words histogram, the no. of sentences containing the words and knowledge corpus. The text summary is extracted using the sentence vectorization process. The sentence vectorization gives the sentences that have at least one of the key words in the sentence from the entire document. The algorithm works fine for the textual matter in the document in MS Notepad format. Factual information that is normally covered under double inverted comas is also given due attention in text summary.

Document summarization is an important step while clustering the large no. of digital documents data base. Documents are clustered in accordance with their contents using the document text summary. The document summarization involves the knowledge corpus scheme comprising of corpus coverage, sentence coverage and term coverage weight. Further, three new weights are introduced as super sentence coverage weight, super corpus coverage weight and super term coverage weight. Super coverage weight is based on synonyms of the key words. The quality of document summary improves and diversified when synonyms of key words are also given due weightage in the process of text processing. The evaluation for the document summary quality is based on inner content metrics precision, recall, F-measure method.