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