COVERAGE, DIVERSITY, AND COHERENCE OPTIMIZATION FOR MULTI-DOCUMENT SUMMARIZATION
| Main Authors: | Umam, Khoirul, Putro, Fidi Wincoko, Pratamasunu, Gulpi Qorik Oktagalu, Arifin, Agus Zainal, Purwitasari, Diana |
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| Format: | Article info application/pdf eJournal |
| Bahasa: | eng |
| Terbitan: |
Faculty of Computer Science - Universitas Indonesia
, 2015
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| Subjects: | |
| Online Access: |
http://jiki.cs.ui.ac.id/index.php/jiki/article/view/278 http://jiki.cs.ui.ac.id/index.php/jiki/article/view/278/323 |
Daftar Isi:
- 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.