Multi-document summarization using evolutionary multi-objective optimization

Jung, Chihoon; Datta, Rituparna; Segev, Aviv

doi:10.1145/3067695.3082040

Cited by 7 publications

(8 citation statements)

References 4 publications

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“…On the other hand, in Multi-objective optimization more than one objective function are optimized simultaneously. Recently, multi-objective evolutionary algorithms have attracted a lot of researches by their ability to approximate a set of Pareto solutions (nondominated solutions) [58] such as Non-dominated Sorting Genetic Algorithm-II (NSGA-II) [8], [56], Multi-Objective Artificial Bee Colony [59], and Ant Colony optimization [11]. The results of this approach are very promising.…”

Section: ) Global-based Text Summarizationmentioning

confidence: 99%

“…sentences) such as euclidean distance, cosine similarity, and Jaccard correlation [75]. However, cosine similarity is the most widely used [8], [53], [55], [57], [59], [76], [77]. The cosine similarity is used to measure the similarity between sentences by performing the inner product between their vectors, then the product normalized by the length of their vectors.…”

Section: Similarity Measurementioning

confidence: 99%

“…Moreover, with limited summary length, and many important sentences, a strategy is needed to select the best summary rather than the best sentences. Selecting the best summary which contains the most important sentences with maximum coverage and minimum redundancy is considered a global optimization problem [8].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Extractive Multi-Document Arabic Text Summarization Using Evolutionary Multi-Objective Optimization With K-Medoid Clustering

2020

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Section: ) Global-based Text Summarizationmentioning

confidence: 99%

Section: Similarity Measurementioning

confidence: 99%

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Extractive Multi-Document Arabic Text Summarization Using Evolutionary Multi-Objective Optimization With K-Medoid Clustering

2020

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“…Rautray and Balabantaray (2017) and Sanchez-Gomez et al 2017proposed nature inspired optimisation-based approach cat swarm optimisation (CSO) and artificial bee colony optimisation (ABC) approach to address the multi-objective optimisation issue. One more multi-objective summarisation is found which uses the k-means clustering algorithm to extract the main topics from documents (Jung et al, 2017). Ghalehtaki et al (2014) proposed a combination method of PSO and cellular learning automata to recognise sentence richness and build summary close to human-generated summary.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy logic-based single document summarisation with improved sentence scoring technique

Patel

Chhinkaniwala

2018

IJKEDM

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Text summarisation is compressed or condensed version of any text document. Due to increasing use of digitisation, massive amount of information is available on internet. Text summarisation is an emerging alternative for users to find relative information in automated shortened versions. In this paper we propose single document summarisation technique using shallow features of sentence to generate summary. The weight of sentences is calculated by applying score of different words and sentence-based statistical features. Here, most salient sentences are selected based on weight of sentences and are put together to generate summary. This is modeled using fuzzy inference system. This approach utilises fuzzy inference and fuzzy measures to find most significant sentences. The result of our proposed method is compared with other methods using recall oriented understudy for Gisting evaluation (ROUGE-N) measures on document understanding conferences (DUC) 2002 dataset and results show that our proposed method outperforms a few baseline methods.

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“…To solve a generic extractive summarization problem, the authors propose a model using evolutionary multi-objective optimization. Multi-objective optimization approach to a text summary generation task is gaining attention recently from the research community [4], [5], [6]. Previous research directions mainly focus on applying and testing diverse optimization methods within the multi-objective problem formulation.…”

Section: Introductionmentioning

confidence: 99%

Knowledge Base Driven Automatic Text Summarization using Multi-objective Optimization

Jung¹,

Yoon²,

Datta³

et al. 2021

IJACSA

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Automatic Text summarization aims to automatically generate condensed summary from a large set of documents on the same topic. We formulate text summarization task as a multi-objective optimization problem by defining information coverage and diversity as two conflicting objective functions. With this formulation, we propose a novel technique to improve the performance using a knowledge base. The main rationale of the approach is to extract important text features of the original text by detecting important entities in a knowledge base. Next, an improvement on the multi-objective optimization algorithm is also proposed for the automatic text summarization problem. The focus is on improving efficiency of the each steps in the evolutionary multi-objective optimization process which is applicable to all tasks with the same problem formulation. The result summary of the suggested method ensure the maximum coverage of the original documents and the diversity of the sentences in the summary among each other. The experiments on DUC2002 and DUC2004 multi-document summarization task dataset shows that the proposed model is effective compared to other methods.

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Multi-document summarization using evolutionary multi-objective optimization

Cited by 7 publications

References 4 publications

Extractive Multi-Document Arabic Text Summarization Using Evolutionary Multi-Objective Optimization With K-Medoid Clustering

Extractive Multi-Document Arabic Text Summarization Using Evolutionary Multi-Objective Optimization With K-Medoid Clustering

Fuzzy logic-based single document summarisation with improved sentence scoring technique

Knowledge Base Driven Automatic Text Summarization using Multi-objective Optimization

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