Title :
Word Sequence Models for Single Text Summarization
Author :
Garcia-Hernandez, R.A. ; Ledeneva, Yulia
Author_Institution :
Autonomous Univ. of the State of Mexico, Tianguistenco
Abstract :
The main problem for generating an extractive automatic text summary is to detect the most relevant information in the source document. For such purpose, recently some approaches have successfully employed the word sequence information from the self-text for detecting the candidate text fragments for composing the summary. In this paper, we employ the so-called n-grams and maximal frequent word sequences as features in a vector space model in order to determine the advantages and disadvantages for extractive text summarization.
Keywords :
sequences; text analysis; candidate text fragment detection; feature vector space model; maximal frequent word sequence model; n-gram word sequence; single extractive text summarization; source document; Data mining; Government; Humans; Internet; Robustness; Supervised learning; Text analysis; Text mining; Web pages; Extractive summarization; maximal frequent sequences; text mining; text models;
Conference_Titel :
Advances in Computer-Human Interactions, 2009. ACHI '09. Second International Conferences on
Conference_Location :
Cancun
Print_ISBN :
978-1-4244-3351-3
Electronic_ISBN :
978-0-7695-3529-6
DOI :
10.1109/ACHI.2009.58
