Title :
Research on Extension LexRank in Summarization for Opinionated Texts
Author :
Xu Liang ; Youli Qu ; Guixiang Ma
Author_Institution :
Sch. of Comput. & Inf. Technol., Beijing Jiaotong Univ., Beijing, China
Abstract :
Along with the boom of information, it is an important task to summarize the valuable information of opinionated texts. Firstly, we model the opinionated text by TAM and get the topic and aspect attribute of every sentence. Secondly, we successively used the basic LexRank, Comparative LexRank, Topic-sensitive tf*idf LexRank and Topic-sensitive tf*idf & Comparative LexRank to generate summary. Experimental results show that the best summary on precision can be gotten by the topic-sensitive tf*idf LexRank and the best result on recall and F-measure can be gotten by the topic-sensitive tf*idf & comparative LexRank.
Keywords :
probability; text analysis; TAM; Topic-sensitive tf*idf LexRank; basic LexRank; comparative LexRank; extension LexRank; opinionated texts; probabilistic topic model; topic- aspect model; Computational modeling; Computers; Educational institutions; Equations; Internet; Mathematical model; Medical services; contrast aspects; model text; multi-topic; summarization;
Conference_Titel :
Parallel and Distributed Computing, Applications and Technologies (PDCAT), 2012 13th International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-0-7695-4879-1
DOI :
10.1109/PDCAT.2012.117
