Paper: Fast Joint Compression and Summarization via Graph Cuts

ACL ID D13-1156
Title Fast Joint Compression and Summarization via Graph Cuts
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2013
Authors

Extractive summarization typically uses sen- tences as summarization units. In contrast, joint compression and summarization can use smaller units such as words and phrases, re- sulting in summaries containing more infor- mation. The goal of compressive summariza- tion is to find a subset of words that max- imize the total score of concepts and cut- ting dependency arcs under the grammar con- straints and summary length constraint. We propose an efficient decoding algorithm for fast compressive summarization using graph cuts. Our approach first relaxes the length con- straint using Lagrangian relaxation. Then we propose to bound the relaxed objective func- tion by the supermodular binary quadratic pro- gramming problem, which can be solved ef- ficiently using graph max-flow/min-cut. S- inc...