Clustering and Visualization in a Multi-lingual Multi-document Summarization System

Abstract

To measure the similarity of words, sentences, and documents is one of the major issues in multi-lingual multi-document summarization. This paper presents five strategies to compute the multilingual sentence similarity. The experimental results show that sentence alignment without considering the word position or order in a sentence obtains the best performance. Besides, two strategies are proposed for multilingual document clustering. The two-phase strategy (translation after clustering) is better than one-phase strategy (translation before clustering). Translation deferred to sentence clustering, which reduces the propagation of translation errors, is most promising. Moreover, three strategies are proposed to tackle the sentence clustering. Complete link within a cluster has the best performance, however, the subsumption-based clustering has the advantage of lower computation complexity and similar performance. Finally, two visualization models (i.e., focusing and browsing), which consider the users’ language preference, are proposed.