Word Sequence Models for Single Text Summarization

Author

Garcia-Hernandez, R.A. ; Ledeneva, Yulia

Author_Institution

Autonomous Univ. of the State of Mexico, Tianguistenco

fYear

2009

fDate

1-7 Feb. 2009

Firstpage

44

Lastpage

48

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/ACHI.2009.58

Filename

4782490