CDDS: Constraint-driven document summarization models

Alguliyev, Rasim; Alıguliyev, Ramiz M.; Isazade, Nijat R.

doi:10.1016/j.eswa.2012.07.049

Cited by 38 publications

(16 citation statements)

References 46 publications

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“…An optimization approach to text summarization also has been widely investigated in literature. For example, in Alguliev, Aliguliyev, and Hajirahimova (, ), Alguliev, Aliguliyev, Hajirahimova, and Mehdiyev (), Alguliev, Aliguliyev, and Isazade (, , , , ), and Alguliev, Aliguliyev, and Mehdiyev (, , ), the authors formalized the sentence selection task as an optimization problem and solved the problem by using evolutionary and swarm optimization algorithms. In Alguliev, Aliguliyev, and Mehdiyev (), document summarization is modelled as a non‐linear 0–1 programming problem where the objective function is defined as Heronian mean of the objective functions enforcing the coverage and diversity.…”

Section: Related Workmentioning

confidence: 99%

“…The method maximum coverage and less redundancy proposed in Alguliev, Aliguliyev, and Hajirahimova (, ) models document summarization as a quadratic Boolean programming problem where objective function is a weighted combination of the content coverage and redundancy objectives. Another successful constraint‐driven document summarization model is presented in Alguliev, Aliguliyev, and Isazade () where the document summarization is modelled as a quadratic integer programming problem and solved with discrete binary PSO algorithm. The 0–1 non‐linear model (Alguliev, Aliguliyev, & Isazade, ) uses a weighted linear combination of the arithmetic and geometric means of the coverage and diversity objectives to aggregate them into a single objective function.…”

Section: Related Workmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

COSUM: Text summarization based on clustering and optimization

et al. 2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…Text summarization is also found to be widely researched topic, including techniques based on natural language analysis (DeJong 1978; Barzilay and Elhadad 1997), semantics (Marcu 1998), discourse (Marcu 1998), ontology (Jishma Mohan et al 2016;Baralis et al 2013), graph (Erkan and Radev 2004;Wang et al 2013), Wikipedia (Sankarasubramaniam et al 2014) etc. Recent research papers are available on text summarization based on various global optimization techniques, namely, Quadratic integer programming (QIP) (Alguliev et al 2013), integer programming (Alguliev et al 2011a, b), Genetic algorithms (GAs) (Mendoza et al 2014;Alguliev et al 2014), differential evolution (DE) (Alguliev et al 2011a(Alguliev et al , b, 2012, artificial bee colony (ABC) optimization (Karaboga and Basturk 2007;Chakraborti and Dey 2015) etc. These global optimization-based techniques have generated better results vis-à-vis greedy techniques, for abstractive summaries based on standard data sets, e.g., DUC.…”

Section: Automatic Text Summarizationmentioning

confidence: 99%

Analysis of Competitor Intelligence in the Era of Big Data: An Integrated System Using Text Summarization Based on Global Optimization

Chakraborti

Dey

2018

Bus Inf Syst Eng

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Automatic text summarization can be applied to extract summaries from competitor intelligence (CI) corpora that organizations create by gathering textual data from the Internet. Such a representation of CI text is easier for managers to interpret and use for making decisions. This research investigates design of an integrated system for CI analysis which comprises clustering and automatic text summarization and evaluates quality of extractive summaries generated automatically by various text-summarization techniques based on global optimization. This research is conducted using experimentation and empirical analysis of results. A survey of practicing managers is also carried out to understand the effectiveness of automatically generated summaries from CI perspective. Firstly, it shows that global optimization-based techniques generate good quality extractive summaries for CI analysis from topical clusters created by the clustering step of the integrated system. Secondly, it shows the usefulness of the generated summaries by having them evaluated by practicing managers from CI perspective. Finally, the implication of this research from the point of view of theory and practice is discussed.

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“…Rasim M. Alguliev [18], in which the objective function is a weighted combination of (1) content coverage, and (2) for redundancy objectives. In another work [19] they proposed CDDS based summarization with two objectives diversity and coverage. In summarization, similarity evaluation among of sentences is a laborious task because of (1) complex sentence structure, and (2) lack of extra information so, Ming Che Lee [20] had been proposed "Transformed Vector Space" model based on Word-Net.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Approach for Single Text Document Summarization Using Statistical and Sentiment Features

Yadav

Sharan

2015

International Journal of Information Retrieval Research

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Summarization is a way to represent same information in concise way with equal sense. This can be categorized in two type Abstractive and Extractive type. Our work is focused around Extractive summarization. A generic approach to extractive summarization is to consider sentence as an entity, score each sentence based on some indicative features to ascertain the quality of sentence for inclusion in summary. Sort the sentences on the score and consider top n sentences for summarization. Mostly statistical features have been used for scoring the sentences. A hybrid model for a single text document summarization is being proposed. This hybrid model is an extraction based approach, which is combination of Statistical and semantic technique. The hybrid model depends on the linear combination of statistical measures: sentence position, TF-IDF, Aggregate similarity, centroid, and semantic measure. The idea to include sentiment analysis for salient sentence extraction is derived from the concept that emotion plays an important role in communication to effectively convey any message hence, it can play a vital role in text document summarization. For comparison, five system summaries have been generated: Proposed Work, MEAD system, Microsoft system, OPINOSIS system, and Human generated summary, and evaluation is done using ROUGE score.

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CDDS: Constraint-driven document summarization models

Cited by 38 publications

References 46 publications

COSUM: Text summarization based on clustering and optimization

COSUM: Text summarization based on clustering and optimization

Analysis of Competitor Intelligence in the Era of Big Data: An Integrated System Using Text Summarization Based on Global Optimization

Hybrid Approach for Single Text Document Summarization Using Statistical and Sentiment Features

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