Recent Developments in Text Generation and Summarization

Alan Turing (for whom the equivalent of the Nobel Prize in Computer Science is named) proposed a test for artificial intelligence in 1950. This test, known as the Turing Test, says that if humans ask questions and cannot distinguish between text responses generated by a machine and a human, then that machine can be deemed to be intelligent.

Text generation using very large models, such as the GPT-2 (with around 1.5 billion parameters) and BERT (Bidirectional Encoder Representation from Transformers) (with around 340 million parameters), can aid in auto-completion tasks. Auto-completion presents unique ethical challenges. While it can offer convenience, it can also reinforce biases in the data. This is accentuated by the fact that most user experience layouts can show only a limited number of options. Furthermore, auto-completion can controversially suggest responses that are different from what the sender originally wants...