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The Decomposition of Human-Written Summary Sentences

Jing, Hongyan; McKeown, Kathleen

We dene the problem of decomposing human-written summary sentences and propose a novel Hidden Markov Model solution to the problem. Human summarizers often rely on cutting and pasting of the full document to generate summaries. Decomposing a human-written summary sentence requires determining: (1) whether it is constructed by cutting and pasting, (2) what components in the sentence come from the original document, and (3) where in the document the components come from. Solving the decomposition problem can potentially lead to the automatic acquisition of large corpora for summarization. It also sheds light on the generation of summary text by cutting and pasting. The evaluation shows that the proposed decomposition algorithm performs well.


More About This Work

Academic Units
Computer Science
Proceedings of SIGIR'99
Published Here
May 3, 2013
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