Paper: Automatically Determining a Proper Length for Multi-Document Summarization: A Bayesian Nonparametric Approach

ACL ID D13-1069
Title Automatically Determining a Proper Length for Multi-Document Summarization: A Bayesian Nonparametric Approach
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2013
Authors

Document summarization is an important task in the area of natural language processing, which aims to extract the most important in- formation from a single document or a clus- ter of documents. In various summarization tasks, the summary length is manually de- fined. However, how to find the proper sum- mary length is quite a problem; and keeping all summaries restricted to the same length is not always a good choice. It is obvi- ously improper to generate summaries with the same length for two clusters of docu- ments which contain quite different quantity of information. In this paper, we propose a Bayesian nonparametric model for multi- document summarization in order to automat- ically determine the proper lengths of sum- maries. Assuming that an original document can be reconstructed ...