Paper: Fast and Robust Compressive Summarization with Dual Decomposition and Multi-Task Learning

ACL ID P13-1020
Title Fast and Robust Compressive Summarization with Dual Decomposition and Multi-Task Learning
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013
Authors

We present a dual decomposition frame- work for multi-document summarization, using a model that jointly extracts and compresses sentences. Compared with previous work based on integer linear pro- gramming, our approach does not require external solvers, is significantly faster, and is modular in the three qualities a sum- mary should have: conciseness, informa- tiveness, and grammaticality. In addition, we propose a multi-task learning frame- work to take advantage of existing data for extractive summarization and sentence compression. Experiments in the TAC- 2008 dataset yield the highest published ROUGE scores to date, with runtimes that rival those of extractive summarizers.