Graph coloring and ACO based summarization for social networks

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

doi:10.1016/j.eswa.2017.01.010

Cited by 34 publications

(13 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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show abstract

“…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%

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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“…For example, it can be used to register medical or biometric images [16,4] and to find good resource allocation scheme for device-to-device (D2D) communications [22,3], used in modern wireless communication systems [11]. Moreover, graph coloring is extensively used in social networks problem such as Community Identification in Dynamic Social Networks [21], summarization of social networks messages [19], and for Collective Spammer Detection [6]. A common characteristic of these tasks is that the graphs have very large size, thus requiring a speed up of the traditional greedy sequential coloring heuristics [2], obtained introducing parallelization.…”

Section: Introductionmentioning

confidence: 99%

A Parallel MCMC Algorithm for the Balanced Graph Coloring Problem

Conte

Grossi

Lanzarotti

et al. 2019

Graph-Based Representations in Pattern Recognition

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In parallel computation domain, graph coloring is widely studied in its own and represents a reference problem for scheduling of parallel tasks. Unfortunately, common graph coloring strategies usually focus on minimizing the number of colors without any concern for the sizes of each color class, thus producing highly skewed color class distributions. However, to guarantee efficiency in parallel computations, but also in other application contexts, it is important to keep the color classes highly balanced in their sizes. In this paper we address this challenging issue for large scale graphs, proposing a fast parallel MCMC heuristic for sparse graphs that randomly generates good balanced colorings provided that a sufficient number of colors are made available. We show its effectiveness through some numerical simulations on random graphs.

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Graph coloring and ACO based summarization for social networks

Cited by 34 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 Parallel MCMC Algorithm for the Balanced Graph Coloring Problem

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