Multi-document Summarization Exploiting Semantic Analysis Based on Tag Cluster

Abstract

Multi-document summarization techniques aim to reduce the documents into a small set of words or paragraphs that convey the main meaning of the original documents. Many approaches for multi-document summarization have used probability based methods and machine learning techniques to summarize multiple documents sharing a common topic at the same time. However, these techniques fail to semantically analyze proper nouns and newly-coined words because most of them depend on old-fashioned dictionary or thesaurus. To overcome these drawbacks, we propose a novel multi-document summarization technique which employs the tag cluster on Flickr, a kind of folksonomy systems, for detecting key sentences from multiple documents. We first create a word frequency table for analyzing the semantics and contribution of words by using HITS algorithm. Then, by exploiting tag clusters, we analyze the semantic relationship between words in the word frequency table. The experimental results on TAC 2008, 2009 data sets demonstrate the improvement of our proposed framework over existing summarization systems.