Paper: Jointly Learning to Extract and Compress

ACL ID P11-1049
Title Jointly Learning to Extract and Compress
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2011
Authors

We learn a joint model of sentence extraction and compression for multi-document summa- rization. Our model scores candidate sum- maries according to a combined linear model whose features factor over (1) the n-gram types in the summary and (2) the compres- sions used. We train the model using a margin- based objective whose loss captures end sum- mary quality. Because of the exponentially large set of candidate summaries, we use a cutting-plane algorithm to incrementally de- tect and add active constraints efficiently. In- ference in our model can be cast as an ILP and thereby solved in reasonable time; we also present a fast approximation scheme which achieves similar performance. Our jointly extracted and compressed summaries outper- form both unlearned baselines and our learned extract...