This document discusses natural language generation (NLG) tasks and neural approaches. It begins with a recap of language models and decoding algorithms like beam search and sampling. It then covers NLG tasks like summarization, dialogue generation, and storytelling. For summarization, it discusses extractive vs. abstractive approaches and neural methods like pointer-generator networks. For dialogue, it discusses challenges like genericness, irrelevance and repetition that neural models face. It concludes with trends in NLG evaluation difficulties and the future of the field.