The first three authors have contributed equally to this work.1 https://github.com/PrekshaNema25/ StructuredData_To_Descriptions

Structured data summarization involves generation of natural language summaries from structured input data. In this work, we consider summarizing structured data occurring in the form of tables as they are prevalent across a wide variety of domains. We formulate the standard table summarization problem, which deals with tables conforming to a single predefined schema. To this end, we propose a mixed hierarchical attention based encoderdecoder model which is able to leverage the structure in addition to the content of the tables. Our experiments on the publicly available WEATHERGOV dataset show around 18 BLEU (∼ 30%) improvement over the current state-of-the-art.

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Accurate forecasts of infections for localized regions are valuable for policy making and medical capacity planning. Existing compartmental and agent-based models for epidemiological forecasting employ static parameter choices and cannot be readily contextualized, while adaptive solutions focus primarily on the reproduction number. In the current work, we propose a novel model-agnostic Bayesian optimization approach for learning model parameters from observed data that generalizes to multiple application-specific fidelity criteria. Empirical results demonstrate the efficacy of the proposed approach with SEIR-like compartmental models on COVID-19 case forecasting tasks. A city-level forecasting system based on this approach is being used for COVID-19 response in a few highly impacted Indian cities.