COVERAGE, DIVERSITY, AND COHERENCE OPTIMIZATION FOR MULTI-DOCUMENT SUMMARIZATION

Abstrak

A great summarization on multi-document with similar topics can help users to get useful in¬for¬ma¬tion. A good summary must have an extensive coverage, minimum redundancy (high diversity), and smooth connection among sentences (high coherence). Therefore, multi-document summarization that con¬siders the coverage, diversity, and coherence of summary is needed. In this paper we pro¬pose a novel method on multi-document summarization that optimizes the coverage, diversity, and co¬her¬ence among the summary's sentences simultaneously. It integrates self-adaptive differential evo¬lu¬tion (SaDE) al¬gorithm to solve the optimization problem. Sentences ordering algorithm based on top¬ic¬al closeness ap¬proach is performed in SaDE iterations to improve coherences among the summary's sen¬tences. Ex¬pe¬ri¬ments have been performed on Text Analysis Conference (TAC) 2008 data sets. The ex¬perimental re¬sults showed that the proposed method generates summaries with average coherence and ROUGE scores 29-41.2 times and 46.97-64.71% better than any other method that only consider coverage and di¬versity, re-spect¬ive¬ly.