T<sup>3</sup>: Domain-Agnostic Neural Time-series Narration

Sharma, Mandar; Brownstein, John S.; Ramakrishnan, Naren

doi:10.1109/icdm51629.2021.00165

Cited by 1 publication

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“…Fig. 1: Narration of COVID19 Progression using DTG [7] Textual representations of information, for easier assimilation, are often presented in stitched annotations called narratives. The automated generation of such narratives, although serving several niches, as will be discussed, are most prevalent in the public eye through the practice of robojournalism [8], [9].…”

Section: Introduction 1what Is Data-to-text Generation?mentioning

confidence: 99%

Innovations in Neural Data-to-text Generation

Sharma¹,

Gogineni²,

Ramakrishnan³

2022

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The neural boom that has sparked natural language processing (NLP) research through the last decade has similarly led to significant innovations in data-to-text generation (DTG). This survey offers a consolidated view into the neural DTG paradigm with a structured examination of the approaches, benchmark datasets, and evaluation protocols. This survey draws boundaries separating DTG from the rest of the natural language generation (NLG) landscape, encompassing an up-to-date synthesis of the literature, and highlighting the stages of technological adoption from within and outside the greater NLG umbrella. With this holistic view, we highlight promising avenues for DTG research that not only focus on the design of linguistically capable systems but also systems that exhibit fairness and accountability.Index Terms-Data-to-text generation (DTG), natural language generation (NLG) ! 1. This survey exclusively focuses on academic innovations for datato-text generation as the technologies underlying commercial frameworks are often proprietary.

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Section: Introduction 1what Is Data-to-text Generation?mentioning

confidence: 99%