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
Link To Document :
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