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Extracting Summary Sentences Based on
the Document Semantic Graph
January 1, 2005
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Download PDF
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BibTex
Authors
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Natasa Milic-Frayling
Publication Type
TechReport
Pages
9
Number
MSR-TR-2005-07

Abstract
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Related Info
Abstract
We present a method for extracting sentences from an individual document to serve as a
document summary or a pre-cursor to creating a generic document abstract. We apply syntactic
analysis of the text that produces a logical form analysis for each sentence. We use
subject–object–predicate (SOP) triples from individual sentences to create a semantic graph
of the original document and the corresponding human extracted summary. Using the Support
Vector Machines learning algorithm, we train a classifier to identify SOP triples from the
document semantic graph that belong to the summary. The classifier is then used for automatic
extraction of summaries from test documents. Our experiments with the DUC 2002 and CAST
datasets show that including semantic properties and topological graph properties of logical
triples yields statistically significant improvement of the micro-average F1 measure for both the
extraction of SOP triples that correspond to the semantic structure of extracts and the extraction
of summary sentences. Evaluation based on ROUGE shows similar results for the extracted
summary sentences.
Related Info
Related Files
 tr-2005-07.doc
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 Microsoft Research Lab - Cambridge
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