• DocumentCode
    2910408
  • Title

    Context-Based Persian Multi-document Summarization (Global View)

  • Author

    Poormasoomi, Asef ; Kahani, Mohsen ; Yazdi, Saeed Varasteh ; Kamyar, Hossein

  • Author_Institution
    Comput. Eng. Dept., Ferdowsi Univ. of Mashhad, Mashhad, Iran
  • fYear
    2011
  • fDate
    15-17 Nov. 2011
  • Firstpage
    145
  • Lastpage
    149
  • Abstract
    Multi-document summarization is the automatic extraction of information from multiple documents of the same topic. This paper proposes a new method, using LSA, for extracting the global context of a topic and removes sentence redundancy using SRL and WordNet semantic similarity for Persian language. In the previous approaches, the focus was on the sentence features (local view) as the main and basic unit of text. In this paper, the sentences are selected based on the main context hidden in the all documents of a topic. The experimental results show that our proposed method outperforms other Persian multi-document systems.
  • Keywords
    natural language processing; text analysis; word processing; Persian language; SRL; WordNet semantic similarity; automatic information extraction; context-based Persian multidocument summarization; semantic role labeling; sentence features; sentence redundancy; Computers; Context; Educational institutions; Humans; Redundancy; Semantics; Vectors; LSA; Multi-document summarization; Semantic Role labeling; Semantic Similarity;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Asian Language Processing (IALP), 2011 International Conference on
  • Conference_Location
    Penang
  • Print_ISBN
    978-1-4577-1733-8
  • Type

    conf

  • DOI
    10.1109/IALP.2011.53
  • Filename
    6121490