Toward Abstractive Summarization Using Semantic Representations

Fei Liu, Jeffrey Flanigan, Sam Thomson, Norman Sadeh, Noah A. Smith
2015 Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies  
We present a novel abstractive summarization framework that draws on the recent development of a treebank for the Abstract Meaning Representation (AMR). In this framework, the source text is parsed to a set of AMR graphs, the graphs are transformed into a summary graph, and then text is generated from the summary graph. We focus on the graph-tograph transformation that reduces the source semantic graph into a summary graph, making use of an existing AMR parser and assuming the eventual
more » ... ity of an AMR-totext generator. The framework is data-driven, trainable, and not specifically designed for a particular domain. Experiments on goldstandard AMR annotations and system parses show promising results. Code is available at: https://github.com/summarization
doi:10.3115/v1/n15-1114 dblp:conf/naacl/0004FTSS15 fatcat:7gvqhbwubfacxj3frut22obx5u