Paper Info
Title
Automatic Classification of Cross-document Structural Relations for Discussion Summarization.
Abstract
We propose a method to summarize threaded texts, particularly online discussions (forums and blogs) and e-mail conversations. These texts have semantic relations between their sentences since each post replies to another one. While cross-document structural theory (CST) concerns about links in documents, this work examines the use of CST in summarizing threaded document since no one use it in this domain before. To do so, this work has two phases: first, we identify and extract four CST relations by using supervised machine learning. Second, we develop a new sentence weighting method based on model selection technique over the identified cross-document relations. The experiments show that using of CST in the thread summarization gives promised results. The performances of all methods were evaluated using ROUGE-a standard evaluation metric used in text summarization.
Year
DOI
Venue
2014
10.3233/978-1-61499-434-3-979
Frontiers in Artificial Intelligence and Applications
Keywords
Field
DocType
Thread summarization,Conversation summarization,Cross-document relation,Model selection,Extraction summary
Automatic summarization,Multi-document summarization,Information retrieval,Computer science,Model selection
Conference
Volume
ISSN
Citations 
265
0922-6389
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Ibrahim Almahy100.34
Naomie Salim242448.23