Text Summarization via Hidden Markov Models and Pivoted QR Matrix Decomposition
Text Summarization via Hidden Markov Models and Pivoted QR Matrix Decomposition
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Date
2001-05-10
Authors
Conroy, John
O'Leary, Dianne P.
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Abstract
A sentence extract summary of a document is a subset of the
document's sentences that contains the main ideas in the
document.
We present two approaches to generating such summaries.
The first uses a pivoted QR decomposition of the term-sentence
matrix in order to identify sentences that have ideas that
are distinct from those in other sentences.
The second is based on a hidden Markov model that judges the
likelihood that each sentence should be contained in the
summary.
We compare the results of these methods with summaries
generated by humans, showing that we obtain higher agreement
than do earlier methods.
(Cross-referenced as UMIACS-TR-2001-11)