Parmar Paresh B., Ketan Patel
Abstract: We refer text mining as the discovery by computer of new, previously unknown information, by automatically extracting information from a usually large amount of different unstructured textual resources. extraction and concept finding in learning objects is one of the most important subjects in eLearning environments. In this paper a novel model is presented in order to improve concept finding in learning objects. The system develops many approaches to solve this problem that gave a high quality result. The model consists of four stages. The preprocess stages convert the unstructured text into structured. In first stage, the system removes the stop words, pars the text and assigning the POS (tag) for each word in the text and store the result in a table. The second stage is to extract the important key phrases in the text by implementing a new algorithm through ranking the candidate words. The system uses the extracted s/key phrases to select the important sentence. Each sentence ranked depending on many features such as the existence of the s/key phrase in it, the relation between the sentence and the title by using a similarity measurement and other many features. The Third stage of the proposed system is to extract the sentences with the highest rank. The Forth stage is the filtering stage. This stage reduced the amount of the candidate sentences in the summary in order to produce a qualitative summary using KFIDF measurement. A new technique to produce a summary of an original text investigated in this paper.
Keywords: Text Summarization, Key phrases Extraction, Text mining, Data Mining, Text compression