This paper surveys the current state of the art in Natural Language Generation (NLG),
defined as the task of generating text or speech from non-linguistic input. A survey of NLG is …

Figurative language generation (FLG) is the task of reformulating a given text to include a
desired figure of speech, such as a hyperbole, a simile, and several others, while still being …

Data-to-text generation can be conceptually divided into two parts: ordering and structuring
the information (planning), and generating fluent language describing the information …

Recent developments in neural networks have led to the advance in data-to-text generation.
However, the lack of ability of neural models to control the structure of generated output can …

Generating sequential natural language descriptions from graph-structured data (eg,
knowledge graph) is challenging, partly because of the structural differences between the …

The neural attention model has achieved great success in data-to-text generation tasks.
Though usually excelling at producing fluent text, it suffers from the problem of information …

For many new application domains for data-to-text generation, the main obstacle in training
neural models consists of a lack of training data. While usually large numbers of instances …

Recent advancements in data-to-text generation largely take on the form of neural end-to-
end systems. Efforts have been dedicated to improving text generation systems by changing …

In open domain table-to-text generation, we notice the unfaithful generation usually contains
hallucinated entities which can not be aligned to any input table record. We thus try to …

Rumor detection aims to detect rumors in a timely manner to prevent malicious rumors from
misleading the public and disrupting social order. However, rumor detection suffers from the …