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We present a framework for generating natural language description fromstructured data such as tables; the problem comes under the category ofdata-to-text natural language generation (NLG). Modern data-to-text NLG systemstypically employ end-to-end statistical and neural architectures that learnfrom a limited amount of task-specific labeled data, and therefore, exhibitlimited scalability, domain-adaptability, and interpretability. Unlike thesesystems, ours is a modular, pipeline-based approach, and does not requiretask-specific parallel data. It rather relies on monolingual corpora and basicoff-the-shelf NLP tools. This makes our system more scalable and easilyadaptable to newer domains.  Our system employs a 3-staged pipeline that: (i) converts entries in thestructured data to canonical form, (ii) generates simple sentences for eachatomic entry in the canonicalized representation, and (iii) combines thesentences to produce a coherent, fluent and adequate paragraph descriptionthrough sentence compounding and co-reference replacement modules. Experimentson a benchmark mixed-domain dataset curated for paragraph description fromtables reveals the superiority of our system over existing data-to-textapproaches. We also demonstrate the robustness of our system in accepting otherpopular datasets covering diverse data types such as Knowledge Graphs andKey-Value maps.

Scalable Micro-planned Generation of Discourse from Structured Data