COVERAGE, DIVERSITY, AND COHERENCE OPTIMIZATION FOR MULTI-DOCUMENT SUMMARIZATION
Abstract: 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.
Keywords: multi-document
summarization, optimization, self-adaptive differential evolution, sentences
ordering, topical closeness
Penulis: Khoirul Umam, Fidi
Wincoko Putro, Gulpi Qorik Oktagalu Pratamasunu, Agus Zainal Arifin, Diana
Purwitasari
Kode Jurnal: jptkomputergg150002
