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

Khoirul Umam, Fidi Wincoko Putro, Gulpi Qorik Oktagalu Pratamasunu, Agus Zainal Arifin, Diana Purwitasari

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

Full Text:

PDF


DOI: http://dx.doi.org/10.21609/jiki.v8i1.278

Refbacks

  • There are currently no refbacks.

Comments on this article

View all comments


Copyright © Jurnal Ilmu Komputer dan Informasi. Faculty of Computer Science Universitas Indonesia.

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

View JIKI Statistic