DocumentCode
2616663
Title
Intelligent Extractive Text Summarization Using Fuzzy Inference Systems
Author
Kiani-B, Arman ; Akbarzadeh-T, M.-R. ; Moeinzadeh, M.H.
Author_Institution
Dept. of Electr. Eng., Mashhad Ferdowsi Univ.
fYear
0
fDate
0-0 0
Firstpage
1
Lastpage
4
Abstract
With the growing size of textual information on the World Wide Web, automatic data summarization becomes essential for both Web users as well as data base and search engine developers. In this paper, we propose a novel approach for extracting the most relevant sentences from an original document to form a summary. The approach utilizes fuzzy measures and inference to find the most significant sentences. Experimental results reveal that the proposed approach extracts the more relevant sentences when compared with two commercially available text summarizers
Keywords
abstracting; fuzzy reasoning; text analysis; fuzzy inference systems; intelligent extractive text summarization; membership functions; parsing; Data engineering; Data mining; Decision trees; Engines; Fuzzy systems; Humans; ISO standards; Natural language processing; Probability; Statistical learning; Fuzzy Inference Systems; Membership Functions; Parsing; Text Summarization;
fLanguage
English
Publisher
ieee
Conference_Titel
Engineering of Intelligent Systems, 2006 IEEE International Conference on
Conference_Location
Islamabad
Print_ISBN
1-4244-0456-8
Type
conf
DOI
10.1109/ICEIS.2006.1703156
Filename
1703156
Link To Document