The decomposition of human-written summary sentences

This work defines the problem of decomposing human-written summary sentences and proposes a novel Hidden Markov Model solution to the problem and sheds light on the generation of summary text by cutting and pasting.

Abstract

We define 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, ( 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.