Paper: Learning to Generate Coherent Summary with Discriminative Hidden Semi-Markov Model

ACL ID C14-1156
Title Learning to Generate Coherent Summary with Discriminative Hidden Semi-Markov Model
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2014
Authors

In this paper we introduce a novel single-document summarization method based on a hidden semi-Markov model. This model can naturally model single-document summarization as the optimization problem of selecting the best sequence from among the sentences in the input doc- ument under the given objective function and knapsack constraint. This advantage makes it possible for sentence selection to take the coherence of the summary into account. In addition our model can also incorporate sentence compression into the summarization process. To demon- strate the effectiveness of our method, we conduct an experimental evaluation with a large-scale corpus consisting of 12,748 pairs of a document and its reference. The results show that our method significantly outperforms the competitive baselines ...