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Meaning Representation (AMR) is a semantic formalism for which a growing set of annotated examples is available. We introduce the first approach to parse sentences into this representation, providing a strong baseline for future improvement. The method is based on a novel algorithm for finding a maximum spanning, connected subgraph, embedded within a Lagrangian relaxation of an optimization problem that imposes linguistically inspired constraints. Our approach is described in the general framework of structured prediction, allowing future incorporation of additional features and constraints, and may extend to other formalisms as well. Our open-source system, JAMR, is available at:http://github.com/jflanigan/jamr

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 availability 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

Language generation from purely semantic representations is a challenging task. This paper addresses generating English from the Abstract Meaning Representation (AMR), consisting of re-entrant graphs whose nodes are concepts and edges are relations. The new method is trained statistically from AMRannotated English and consists of two major steps: (i) generating an appropriate spanning tree for the AMR, and (ii) applying tree-tostring transducers to generate English. The method relies on discriminative learning and an argument realization model to overcome data sparsity. Initial tests on held-out data show good promise despite the complexity of the task. The system is available open-source as part of JAMR at:http://github.com/jflanigan/jamr

Materials science literature contains millions of materials synthesis procedures described in unstructured natural language text. Largescale analysis of these synthesis procedures would facilitate deeper scientific understanding of materials synthesis and enable automated synthesis planning. Such analysis requires extracting structured representations of synthesis procedures from the raw text as a first step. To facilitate the training and evaluation of synthesis extraction models, we introduce a dataset of 230 synthesis procedures annotated by domain experts with labeled graphs that express the semantics of the synthesis sentences. The nodes in this graph are synthesis operations and their typed arguments, and labeled edges specify relations between the nodes. We describe this new resource in detail and highlight some specific challenges to annotating scientific text with shallow semantic structure. We make the corpus available to the community to promote further research and development of scientific information extraction systems.