A Topic-driven Summarization using K-mean Clustering and Tf-Isf Sentence Ranking

Abstract

Enormous online information is available due to the World Wide Web. This needed efficient and accurate summarization systems to extract significant information. Text summarization system automatically generates a summary of a given document and helps people to make effective decisions in less time. In this paper two methods have been proposed for query-focused multi-document summarization that uses k-mean clustering, term-frequency and inversesentence-frequency method for sentence weighting to rank the sentences of the documents with respect to a given query. The proposed method finds the proximity of documents and query, and later uses this proximity to rank sentences of each document. It is assumed that the document which is nearer to a query might contain more meaning full sentences with respect to the information need expressed by user's query further if a sentence contains rare query term than it is more informative than the sentences that contains frequent query term. Both methods first gives weights to documents according to their proximity and use these document weights to rank each of their sentences with tf-idf ranking function. A relative study for proposed methods has been done and experimental results shows that both methods are comparable because of a slight difference in performance. DUC 2007 test dataset and ROUGH-1.5.5 summarization evaluation package is used for evaluation purpose.

High priority tweet detection and summarization in natural disasters

“…SumBasic yönteminde tweet içerisindeki kelimelerin genel kullanım frekanslarının toplamıyla hesaplanan en yüksek değere sahip k adet tweet seçilir [16]. TF-ISF(Term Frequency -Inverse Sentence Frequency) yönteminde tweet içerisindeki kelimenin genel kullanım sıklığı ile tweet içerisinde ki kullanım sıklığı hesaba katılarak özet puanı bulunup aralarından en yüksek puana sahip k adet tweet seçilir [17]. Son olarak Hybrid TF-IDF yöntemi ise Eşitlik(1) de gösterilen TF-IDF yönteminin tweet'ler üzerine özelleştirilmiş halidir.…”