Paper: Multiple Aspect Summarization Using Integer Linear Programming

ACL ID D12-1022
Title Multiple Aspect Summarization Using Integer Linear Programming
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2012
Authors

Multi-document summarization involves many aspects of content selection and sur- face realization. The summaries must be informative, succinct, grammatical, and obey stylistic writing conventions. We present a method where such individual aspects are learned separately from data (without any hand-engineering) but optimized jointly using an integer linear programme. The ILP framework allows us to combine the decisions of the expert learners and to select and rewrite source content through a mixture of objective setting, soft and hard constraints. Experimental results on the TAC-08 data set show that our model achieves state-of-the-art performance using ROUGE and signifi- cantly improves the informativeness of the summaries.