Jayashree.R1, Srikanta Murthy.K2 and Sunny.K1
1Department of Computer Science, PES Institute Of Technology, Bangalore, India
2Department of Computer Science, PES School Of Engineering, Bangalore, India
ABSTRACT
The internet has caused a humongous growth in the number of documents available online. Summaries of documents can help find the right information and are particularly effective when the document base is very large. Keywords are closely associated to a document as they reflect the document's content and act as indices for a given document. In this work, we present a method to produce extractive summaries of documents in the Kannada language, given number of sentences as limitation. The algorithm extracts key words from pre-categorized Kannada documents collected from online resources. We use two feature selection techniques for obtaining features from documents, then we combine scores obtained by GSS (Galavotti, Sebastiani, Simi) coefficients and IDF (Inverse Document Frequency) methods along with TF (Term Frequency) for extracting key words and later use these for summarization based on rank of the sentence. In the current implementation, a document from a given category is selected from our database and depending on the number of sentences given by the user, a summary is generated.
KEYWORDS
Summary, Keywords, GSS coefficient, Term Frequency (TF), IDF (Inverse Document Frequency) and Rank of sentence
ORIGINAL SOURCE URL: http://airccse.org/journal/ijsc/papers/2411ijsc08.pdf
http://airccse.org/journal/ijsc/current2011.html