Multi-Document Text Summarization Using Topic Modeling and Fuzzy Logic Approach
It is tremendous to extract the information faster from internet nowadays. There is lot of materials available on the internet and in order to extract the most relevant information, a good mechanism is found to be used. This problem is settled by the Automatic Text Summarization mechanism. Text Summarization is the system of developing a shorter version of the text that involves the relevant information. Text summarization is classified as Extraction and Abstraction. Here this paper targets on the Topic modeling and Fuzzy logic approach for processing text summarization.
In this paper, the efficient way of summarizing the text document is performed by using topic modeling and fuzzy logic approach and then evaluation of the result with the rouge scores was calculated. Every sentence is enabled with a rank which is based on its importance in the original document. Rank of the sentence is calculated by using Triangular membership function. Sentence selection is involved according to the ranks and the summary are generated. The rouge will generate three scores as, Recall, Precision and F-score. F-score is found to be the evaluation metric for the correctness of a summary. The comparison of three different summaries by compressing the input document as 1/2nd, 1/3rd, 1/4th rouge scores and f-score provides the effective results towards summarizing the text document.
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