Paper: Aggregation Via Set Partitioning For Natural Language Generation

ACL ID N06-1046
Title Aggregation Via Set Partitioning For Natural Language Generation
Venue Human Language Technologies
Session Main Conference
Year 2006
Authors

The role of aggregation in natural lan- guage generation is to combine two or more linguistic structures into a single sentence. The task is crucial for generat- ing concise and readable texts. We present an efficient algorithm for automatically learning aggregation rules from a text and its related database. The algorithm treats aggregation as a set partitioning problem and uses a global inference procedure to find an optimal solution. Our experiments show that this approach yields substan- tial improvements over a clustering-based model which relies exclusively on local information.