Extractive multi-document text summarization using a multi-objective artificial bee colony optimization approach

Sánchez‐Gómez, Jesús; Vega‐Rodríguez, Miguel A.; Pérez, Carlos J.

doi:10.1016/j.knosys.2017.11.029

Cited by 84 publications

(57 citation statements)

References 21 publications

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“…In paper [12] discuss on several optimization methods had been adopted to reducing the time consuming process and produces approximate the Global best solution artificial bee colony [13], Genetic algorithms [14] and cuckoo search optimization [15].Most of the researchers discuss on single-text summarization system that produces patent summaries using extractive summarization.…”

Section: Related Workmentioning

confidence: 99%

Text Summarization using QA Corpus for User Interaction Model QA System

Karpagam¹,

Saradha²,

Manikandan³

et al. 2020

IJEME

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Document summarization is capable of generating user query relevant, precise summaries from the original document for user needs. To reduce the response time summary generation, QA corpus is built for similar questions and answer with help of learning model. It has been trained and tested by Quora duplicate and Yahoo! Answer datasets. The large QA corpus has been dynamically clustered with semantic features paves a way for efficient document's retrieval. Answers are produced from datasets or generate summaries for unanswerable from the available sources. Results obtained from statistical significance test with hypothesis testing and evaluation with standard metrics proves the significant improvement in generating text summarization using QA corpus. The outcome is better in the producing close proximity of answers for the given user query.

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Section: Related Workmentioning

confidence: 99%

Text Summarization using QA Corpus for User Interaction Model QA System

Karpagam¹,

Saradha²,

Manikandan³

et al. 2020

IJEME

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“…However, until now, there are only few studies using MOO approach for summarizing text. One of them is using Multi-Objective Artificial Bee Colony (ABC) algorithm [24]. But the main concern of that study is maximizing content coverage and minimizing redundancies in the summary.…”

Section: Problem and Objectivementioning

confidence: 99%

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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“…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%

“…Peyrard and Eckle-Kohler [55] proposed a general optimization framework to summarize a set of input documents using the ABC algorithm. 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.…”

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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Extractive multi-document text summarization using a multi-objective artificial bee colony optimization approach

Cited by 84 publications

References 21 publications

Text Summarization using QA Corpus for User Interaction Model QA System

Text Summarization using QA Corpus for User Interaction Model QA System

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

Solving Multi-Document Summarization as an Orienteering Problem

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