Ant colony heuristic for user-contributed comments summarization

Mosa, Mohamed Atef; Hamouda, Alaa; Marei, Mahmoud

doi:10.1016/j.knosys.2016.11.009

Cited by 22 publications

(10 citation statements)

References 6 publications

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“…The combination of graph and metaheuristic approach is implemented by [18], [19]. Those studies use graph to construct relation between social media comments then use ACO for selecting the important comments.…”

Section: Related Workmentioning

confidence: 99%

“…Although [14] said the method is abstractive, it can be classified as an extractive method because the new sentences are only generated from available words in the graph. The combination of graph and metaheuristic approach has also been applied by [18], [19]. They are utilizing graph of comments then use ACO algorithm for selecting some important comments from that graph.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Objective Ant Colony Optimization for Automatic Social Media Comments Summarization

Lucky¹,

Girsang²

2019

ijacsa

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Summarizing social media comments automatically can help users to capture important information without reading the whole comments. On the other hand, automatic text summarization is considered as a Multi-Objective Optimization (MOO) problem for satisfying two conflicting objectives. Retaining the information from the source of text as much as possible and producing the summary length as short as possible. To solve that problem, an undirected graph is created to construct the relation between social media comments. Then, the Multi-Objective Ant Colony Optimization (MOACO) algorithm is applied to generate summaries by selecting concise and important comments from the graph based on the desired summary size. The quality of generated summaries is compared to other text summarization algorithms such as TextRank, LexRank, SumBasic, Latent Semantic Analysis, and KL-Sum. The result showed that MOACO can produce informative and concise summaries which have small cosine distance to the source text and fewer number of words compared to the other algorithms.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Objective Ant Colony Optimization for Automatic Social Media Comments Summarization

Lucky¹,

Girsang²

2019

ijacsa

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“…SI has been introduced to text summarization during the last decade. It produced promising results in several studies on different NLP problems, including text summarization [53][54][55][56][57]. The majority of SI-based summarization studies used particle swarm optimization (PSO).…”

Section: Swarm-intelligence-based Approachesmentioning

confidence: 99%

“…Other SI meta-heuristics have also been used with text summarization, including artificial bee colony (ABC) [55,56], ACO [57,59], and cuckoo search (CS) [60]. Peyrard and Eckle-Kohler [55] proposed a general optimization framework to summarize a set of input documents using the ABC algorithm.…”

Section: Swarm-intelligence-based Approachesmentioning

confidence: 99%

“…Sanchez-Gomez et al [56] also proposed an ABC based summarizer by formulating the summarization problem as a multi-objective one. ACO has also been used for single document [61] and short text [57,59] summarization problems. Finally, Rautray and Balabantaray [60] proposed a multi-document summarizer using CS meta-heuristic.…”

Section: Swarm-intelligence-based Approachesmentioning

confidence: 99%

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Solving Multi-Document Summarization as an Orienteering Problem

Al-Saleh

Menaï

2018

Algorithms

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With advances in information technology, people face the problem of dealing with tremendous amounts of information and need ways to save time and effort by summarizing the most important and relevant information. Thus, automatic text summarization has become necessary to reduce the information overload. This article proposes a novel extractive graph-based approach to solve the multi-document summarization (MDS) problem. To optimize the coverage of information in the output summary, the problem is formulated as an orienteering problem and heuristically solved by an ant colony system algorithm. The performance of the implemented system (MDS-OP) was evaluated on DUC 2004 (Task 2) and MultiLing 2015 (MMS task) benchmark corpora using several ROUGE metrics, as well as other methods. Its comparison with the performances of 26 systems shows that MDS-OP achieved the best F-measure scores on both tasks in terms of ROUGE-1 and ROUGE-L (DUC 2004), ROUGE-SU4, and three other evaluation methods (MultiLing 2015). Overall, MDS-OP ranked among the best 3 systems.

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A core firework updating information guided dynamic fireworks algorithm for global optimization

Zhao

Ning

2019

Soft Comput

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Ant colony heuristic for user-contributed comments summarization

Cited by 22 publications

References 6 publications

Multi-Objective Ant Colony Optimization for Automatic Social Media Comments Summarization

Multi-Objective Ant Colony Optimization for Automatic Social Media Comments Summarization

Solving Multi-Document Summarization as an Orienteering Problem

A core firework updating information guided dynamic fireworks algorithm for global optimization

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