Paper Info
Title
Cross-document structural relationship identification using supervised machine learning
Abstract
Multi document analysis has been a field of interest for decades and is still being actively researched until today. One example of such analysis could be for the task of multi document summarization which is meant to represent the concise description of the original documents. In this paper, we will focus on some special properties that multi document articles hold, specifically news articles. Information across news articles reporting on the same story are often related. Cross-document structure theory (CST) gives several relationships between pairs of sentences from different documents. Among them, we focus on four relations namely ''Identity'', ''Overlap'', ''Subsumption'', and ''Description''. Our aim is to automatically identify these CST relationships. We applied three machine learning techniques, i.e. SVM, neural network and our proposed case-based reasoning (CBR) model. Comparison between these techniques shows that the proposed CBR model yields better results.
Year
DOI
Venue
2012
10.1016/j.asoc.2012.06.017
Appl. Soft Comput.
Keywords
Field
DocType
supervised machine learning,original document,multi document summarization,cst relationship,cross-document structural relationship identification,proposed cbr model yield,concise description,multi document article,proposed case-based reasoning,news article,different document,multi document analysis,case based reasoning,neural network,support vector machine,machine learning
Multi-document summarization,Document analysis,Computer science,Support vector machine,Structure (category theory),Artificial intelligence,Natural language processing,Artificial neural network,Case-based reasoning,Machine learning
Journal
Volume
Issue
ISSN
12
10
1568-4946
Citations 
PageRank 
References 
1
0.37
21
Authors
3
Name
Order
Citations
PageRank
Yogan Jaya Kumar1526.11
Naomie Salim242448.23
Basit Raza34310.67