Exploiting relevance, coverage, and novelty for query-focused multi-document summarization

Luo, Wenjuan; Zhuang, Fuzhen; He, Qing; Shi, Zhongzhi

doi:10.1016/j.knosys.2013.02.015

Cited by 30 publications

(23 citation statements)

References 33 publications

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“…On the other hand, coherence aims to generate a coherent text flow. Moreover, balance means that a summary should have the same relative importance of different aspects in the original documents [48]- [52].…”

Section: ) Global-based Text Summarizationmentioning

confidence: 99%

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%

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

2020

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“…It combines the statistical and cognitive‐based techniques for detecting relevant information and for avoiding redundant information it uses for textual entailment. Luo et al () proposed a probabilistic framework to model topic relevance and coverage. In Ferreira et al (), for avoiding information redundancy and providing diversity in a summary, a new sentence clustering algorithm based on a graph model is proposed.…”

Section: Related Workmentioning

confidence: 99%

“…Single‐document summarization can only distill one document into a shorter version, whereas on the contrary, multi‐document summarization can compress a set of documents. Multi‐document summarization can be seen as an enhancement of single‐document summarization and can be used for outlining the information contained in a cluster of documents (Canhasi & Kononenko, ; Luo, Zhuang, He, & Shi, ; Mendoza, Cobos, & León, ). Based on the target audience, summaries may be generic and query‐focused.…”

Section: Introductionmentioning

confidence: 99%

COSUM: Text summarization based on clustering and optimization

et al. 2018

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Text summarization is a process of extracting salient information from a source text and presenting that information to the user in a condensed form while preserving its main content. In the text summarization, most of the difficult problems are providing wide topic coverage and diversity in a summary. Research based on clustering, optimization, and evolutionary algorithm for text summarization has recently shown good results, making this a promising area. In this paper, for a text summarization, a two‐stage sentences selection model based on clustering and optimization techniques, called COSUM, is proposed. At the first stage, to discover all topics in a text, the sentences set is clustered by using k‐means method. At the second stage, for selection of salient sentences from clusters, an optimization model is proposed. This model optimizes an objective function that expressed as a harmonic mean of the objective functions enforcing the coverage and diversity of the selected sentences in the summary. To provide readability of a summary, this model also controls length of sentences selected in the candidate summary. For solving the optimization problem, an adaptive differential evolution algorithm with novel mutation strategy is developed. The method COSUM was compared with the 14 state‐of‐the‐art methods: DPSO‐EDASum; LexRank; CollabSum; UnifiedRank; 0–1 non‐linear; query, cluster, summarize; support vector machine; fuzzy evolutionary optimization model; conditional random fields; MA‐SingleDocSum; NetSum; manifold ranking; ESDS‐GHS‐GLO; and differential evolution, using ROUGE tool kit on the DUC2001 and DUC2002 data sets. Experimental results demonstrated that COSUM outperforms the state‐of‐the‐art methods in terms of ROUGE‐1 and ROUGE‐2 measures.

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“…It combines the statistical and cognitive-based techniques for detecting relevant information and for avoiding redundant information it uses textual entailment. Luo et al (2013) proposed a probabilisticmodeling relevance, coverage, and novelty framework to model topic relevance and coverage, where a reference topic model incorporating query is utilized for dependent sentence relevance measurement. ]…”

Section: Related Workmentioning

confidence: 99%

A Model for Text Summarization

Alguliyev

Alıguliyev

Isazade

et al. 2017

International Journal of Intelligent Information Technologies

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Text summarization is a process for creating a concise version of document(s) preserving its main content. In this paper, to cover all topics and reduce redundancy in summaries, a two-stage sentences selection method for text summarization is proposed. At the first stage, to discover all topics the sentences set is clustered by using k-means method. At the second stage, optimum selection of sentences is proposed. From each cluster the salient sentences are selected according to their contribution to the topic (cluster) and their proximity to other sentences in cluster to avoid redundancy in summaries until the appointed summary length is reached. Sentence selection is modeled as an optimization problem. In this study, to solve the optimization problem an adaptive differential evolution with novel mutation strategy is employed. With a test on benchmark DUC2001 and DUC2002 data sets, the ROUGE value of summaries got by the proposed approach demonstrated its validity, compared to the traditional methods of sentence selection and the top three performing systems for DUC2001 and DUC2002.

show abstract

Exploiting relevance, coverage, and novelty for query-focused multi-document summarization

Cited by 30 publications

References 33 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

COSUM: Text summarization based on clustering and optimization

A Model for Text Summarization

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