  We present a framework for generating natural language description from
structured data such as tables; the problem comes under the category of
data-to-text natural language generation (NLG). Modern data-to-text NLG systems
typically employ end-to-end statistical and neural architectures that learn
from a limited amount of task-specific labeled data, and therefore, exhibit
limited scalability, domain-adaptability, and interpretability. Unlike these
systems, ours is a modular, pipeline-based approach, and does not require
task-specific parallel data. It rather relies on monolingual corpora and basic
off-the-shelf NLP tools. This makes our system more scalable and easily
adaptable to newer domains.
  Our system employs a 3-staged pipeline that: (i) converts entries in the
structured data to canonical form, (ii) generates simple sentences for each
atomic entry in the canonicalized representation, and (iii) combines the
sentences to produce a coherent, fluent and adequate paragraph description
through sentence compounding and co-reference replacement modules. Experiments
on a benchmark mixed-domain dataset curated for paragraph description from
tables reveals the superiority of our system over existing data-to-text
approaches. We also demonstrate the robustness of our system in accepting other
popular datasets covering diverse data types such as Knowledge Graphs and
Key-Value maps.
