Multiple documents summarization based on evolutionary optimization algorithm

Alguliyev, Rasim; Alıguliyev, Ramiz M.; Isazade, Nijat R.

doi:10.1016/j.eswa.2012.09.014

Cited by 102 publications

(62 citation statements)

References 53 publications

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“…Alguliev et al [4] also describes that by considering the similarity between main content of original documents and main content of summary, we will know the importance of summary towards original documents. Moreover, by considering the similarity between main content of original documents with each summary's sentence, we will know the importance of each summary's sentence towards its original documents.…”

Section: Coveragementioning

confidence: 99%

“…It can be calculated by considering similarity between main content in original documents with main content in candidate summary [4]. Radev et al [9] describes that main content of documents set is reflected by its centroid or its term's weight means.…”

Section: Coveragementioning

confidence: 99%

“…These factors are: 1) an extensive coverage; 2) high diversity or minimum redundancy; 3) high coherences among summary's sentences [4]. Summary that have an extensive coverage indicates it has summarized all information from originnal documents.…”

Section: Introductionmentioning

confidence: 99%

“…One of multi-document summarization methods that use this approach is Optimization of Coverage and Diversity for Summarization using Self-adaptive Differential Evolution (OCDsum-SaDE) method that proposed in [4]. In the method, an optimal summary is searched by considering the coverage and diversity of summary's sentences.…”

Section: Introductionmentioning

confidence: 99%

“…This method is inspired by self-adaptive differential evolution (SaDE) algorithm from [4] and sentences ordering algorithm using topical closeness approach in [6]. SaDE algorithm is used to solve the coverage, diversity, and coherence optimization problem.…”

Section: Introductionmentioning

confidence: 99%

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Umam¹,

Putro²,

Pratamasunu³

et al. 2015

Jurnal Ilmu Komputer dan Informasi

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A great summarization on multi-document with similar topics can help users to get useful information. A good summary must have an extensive coverage, minimum redundancy (high diversity), and smooth connection among sentences (high coherence). Therefore, multi-document summarization that considers the coverage, diversity, and coherence of summary is needed. In this paper we propose a novel method on multi-document summarization that optimizes the coverage, diversity, and coherence among the summary's sentences simultaneously. It integrates self-adaptive differential evolution (SaDE) algorithm to solve the optimization problem. Sentences ordering algorithm based on topical closeness approach is performed in SaDE iterations to improve coherences among the summary's sentences. Experiments have been performed on Text Analysis Conference (TAC) 2008 data sets. The experimental results 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 diversity, respectively. Keywords: multi-document summarization, optimization, self-adaptive differential evolution, sentences ordering, topical closeness AbstrakPeringkasan yang baik terhadap dokumen-dokumen dengan topik yang seragam dapat membantu pembaca dalam memperoleh informasi secara cepat. Ringkasan yang baik merupakan ringkasan dengan cakupan pembahasan (coverage) yang luas dan dengan tingkat keberagaman (diversity) serta keterhubungan antarkalimat (coherence) yang tinggi. Oleh karena itu dibutuhkan metode peringkasan multi-dokumen yang mempertimbangkan tingkat coverage, diversity, dan coherence pada hasil ringkasan. Pada paper ini dikembangkan sebuah metode baru dalam peringkasan multi-dokumen dengan mengoptimasi tingkat coverage, diversity, dan coherence antarkalimat hasil ringkasan secara simultan. Optimasi hasil ringkasan dilakukan dengan menggunakan algoritma self-adaptive differential evolution (SaDE

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Section: Coveragementioning

confidence: 99%

Section: Coveragementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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