Paper: DualSum: a Topic-Model based approach for update summarization

ACL ID E12-1022
Title DualSum: a Topic-Model based approach for update summarization
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2012
Authors

Update summarization is a new challenge in multi-document summarization focusing on summarizing a set of recent documents relatively to another set of earlier docu- ments. We present an unsupervised proba- bilistic approach to model novelty in a doc- ument collection and apply it to the genera- tion of update summaries. The new model, called DUALSUM, results in the second or third position in terms of the ROUGE met- rics when tuned for previous TAC competi- tions and tested on TAC-2011, being statis- tically indistinguishable from the winning system. A manual evaluation of the gen- erated summaries shows state-of-the art re- sults for DUALSUM with respect to focus, coherence and overall responsiveness.