Intelligent Database Systems Presenter : JIAN-REN CHEN Authors : Rafael Ferreira a, * , Luciano De Souza Cabral a , Rafael Dueire Lins a , Gabriel Pereira E Silva a , Fred Freitas a , George D.C. Cavalcanti a , Rinaldo Lima a , Steven J. Simske b , Luciano Favaro c 2013.ESA Assessing sentence scoring techniques for extractive text summarization
14
Embed
Assessing sentence scoring techniques for extractive text summarization
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Intelligent Database Systems Lab
Presenter : JIAN-REN CHEN
Authors : Rafael Ferreiraa, *, Luciano De Souza Cabrala, Rafael Dueire Linsa,
Gabriel Pereira E Silvaa, Fred Freitasa, George D.C. Cavalcantia, Rinaldo Limaa,
Steven J. Simskeb, Luciano Favaroc
2013.ESA
Assessing sentence scoring techniques for extractive text summarization
MotivationDue to the huge volume of information in the Internet, it has
become unfeasible to efficiently sieve useful information from the
huge mass of documents.
Text Summarization
- Extractive
- Abstractive
Intelligent Database Systems Lab
Objectives
• We want to introduce 15 sentence scoring methods and assess all of them for extractive text summarization.
Intelligent Database Systems Lab
Methodology – Word scoring
• Word frequency• TF/IDF• Upper case• Proper noun• Word co-occurrence• Lexical similarity
Score(s) =
n-gram
Intelligent Database Systems Lab
Methodology – Sentence scoring
• Cue-phrases• Sentence inclusion of numerical data• Sentence length• Sentence position• Sentence centrality• Sentence resemblance to the title
in summary, in conclusion, our investigationthe best, the most important, according to the study,significantly, important, in particular, hardly, impossible
Score(s) =
Sp( Si) {1 𝑓𝑖𝑟𝑠𝑡 𝑁 𝑠𝑒𝑛𝑡𝑒𝑛𝑐𝑒𝑠0 h𝑜𝑡 𝑒𝑟𝑤𝑖𝑠𝑒
Intelligent Database Systems Lab
Methodology – Graph scoring
• Text rank• Bushy path of the node• Aggregate similarity