System Combination for Multi-document Summarization

Kai Hong, Mitchell Marcus, Ani Nenkova
2015 Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing  
We present a novel framework of system combination for multi-document summarization. For each input set (input), we generate candidate summaries by combining whole sentences from the summaries generated by different systems. We show that the oracle among these candidates is much better than the summaries that we have combined. We then present a supervised model to select among the candidates. The model relies on a rich set of features that capture content importance from different perspectives.
more » ... erent perspectives. Our model performs better than the systems that we combined based on manual and automatic evaluations. We also achieve very competitive performance on six DUC/TAC datasets, comparable to the state-of-the-art on most datasets.
doi:10.18653/v1/d15-1011 dblp:conf/emnlp/HongMN15 fatcat:ykbw4th2q5bk7o64gb37f3fyze