A new multi-document summarisation approach using saplings growing-up optimisation algorithms: Simultaneously optimised coverage and diversity

Hark C., Uçkan T., KARCI A.

JOURNAL OF INFORMATION SCIENCE, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Publication Date: 2022
  • Doi Number: 10.1177/01655515221101841
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, Academic Search Premier, FRANCIS, IBZ Online, Periodicals Index Online, ABI/INFORM, Aerospace Database, Analytical Abstracts, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, EBSCO Education Source, Education Abstracts, Index Islamicus, Information Science and Technology Abstracts, INSPEC, Library and Information Science Abstracts, Library Literature and Information Science, Library, Information Science & Technology Abstracts (LISTA), Metadex, Civil Engineering Abstracts
  • Keywords: Content coverage, document summarisation, document understanding conference, information diversity, multi-criteria optimisation, multi-document summarisation, optimisation model, recall-oriented understudy for gisting evaluation, saplings growing-up algorithm, SENTENCE SELECTION, MAXIMUM COVERAGE
  • Van Yüzüncü Yıl University Affiliated: Yes


Automatic text summarisation is obtaining a subset that accurately represents the main text. A quality summary should contain the maximum amount of information while avoiding redundant information. Redundancy is a severe deficiency that causes unnecessary repetition of information within sentences and should not occur in summarisation studies. Although many optimisation-based text summarisation methods have been proposed in recent years, there exists a lack of research on the simultaneous optimisation of scope and redundancy. In this context, this study presents an approach in which maximum coverage and minimum redundancy, which form the two key features of a rich summary, are modelled as optimisation targets. In optimisation-based text summarisation studies, different conflicting objectives are generally weighted or formulated and transformed into single-objective problems. However, this transformation can directly affect the quality of the solution. In this study, the optimisation goals are met simultaneously without transformation or formulation. In addition, the multi-objective saplings growing-up algorithm (MO-SGuA) is implemented and modified for text summarisation. The presented approach, called Pareto optimal, achieves an optimal solution with simultaneous optimisation. Experimentation with the MO-SGuA method was tested using open-access (document understanding conference; DUC) data sets. Performance success of the MO-SGuA approach was calculated using the recall-oriented understudy for gisting evaluation (ROUGE) metrics and then compared with the competitive practices used in the literature. Testing achieved a 26.6% summarisation result for the ROUGE-2 metric and 65.96% for ROUGE-L, which represents an improvement of 11.17% and 20.54%, respectively. The experimental results showed that good-quality summaries were achieved using the proposed approach.