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An Automated Summarization Assessment Algorithm for Identifying Summarizing Strategies

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  • Asad Abdi
  • Norisma Idris
  • Rasim M Alguliyev
  • Ramiz M Aliguliyev

Abstract

Background: Summarization is a process to select important information from a source text. Summarizing strategies are the core cognitive processes in summarization activity. Since summarization can be important as a tool to improve comprehension, it has attracted interest of teachers for teaching summary writing through direct instruction. To do this, they need to review and assess the students' summaries and these tasks are very time-consuming. Thus, a computer-assisted assessment can be used to help teachers to conduct this task more effectively. Design/Results: This paper aims to propose an algorithm based on the combination of semantic relations between words and their syntactic composition to identify summarizing strategies employed by students in summary writing. An innovative aspect of our algorithm lies in its ability to identify summarizing strategies at the syntactic and semantic levels. The efficiency of the algorithm is measured in terms of Precision, Recall and F-measure. We then implemented the algorithm for the automated summarization assessment system that can be used to identify the summarizing strategies used by students in summary writing.

Suggested Citation

  • Asad Abdi & Norisma Idris & Rasim M Alguliyev & Ramiz M Aliguliyev, 2016. "An Automated Summarization Assessment Algorithm for Identifying Summarizing Strategies," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-34, January.
    • Handle: RePEc:plo:pone00:0145809
    DOI: 10.1371/journal.pone.0145809
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    References listed on IDEAS

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    1. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
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