Automatic Text Summarization using Fuzzy C-Means Clustering

摘要: Automatic text summarization process has been significantly explored throughout the years to cope with staggering increase of virtual data. Text is commonly divided into two areas-Extractive and Abstractive. Extractive processes largely depend on sentence extraction techniques- implementing graph models or sentence-based models. In this paper, a model proposed where ranking procedure adopts fuzzy C-Means (FCM) clustering, an unsupervised classification method, for purpose. The scoring task relies five key features, including Topic Sentence which first novelty model. Furthermore, clustering soft-computing technique that usually used pattern recognition tasks but can be improved by hard membership elements not regarded in similar any previous works, adding presented Standard summary evaluation techniques have gauge precision, recall f-measure FCM compared different summarizers from perspectives. outcome shows surpasses approaches significantly.