  AMR-to-text is one of the key techniques in the NLP community that aims at
generating sentences from the Abstract Meaning Representation (AMR) graphs.
Since AMR was proposed in 2013, the study on AMR-to-Text has become
increasingly prevalent as an essential branch of structured data to text
because of the unique advantages of AMR as a high-level semantic description of
natural language. In this paper, we provide a brief survey of AMR-to-Text.
Firstly, we introduce the current scenario of this technique and point out its
difficulties. Secondly, based on the methods used in previous studies, we
roughly divided them into five categories according to their respective
mechanisms, i.e., Rules-based, Seq-to-Seq-based, Graph-to-Seq-based,
Transformer-based, and Pre-trained Language Model (PLM)-based. In particular,
we detail the neural network-based method and present the latest progress of
AMR-to-Text, which refers to AMR reconstruction, Decoder optimization, etc.
Furthermore, we present the benchmarks and evaluation methods of AMR-to-Text.
Eventually, we provide a summary of current techniques and the outlook for
future research.
