Text Generation

Text generation is a task in the field of natural language processing aimed at generating content that is difficult to distinguish from text written by humans through algorithms. This task utilizes Markov processes or deep generative models such as LSTMs, with recent state-of-the-art methods including BART, GPT, and GAN-based approaches. System evaluation often employs human scoring or automatic metrics such as METEOR, ROUGE, and BLEU. Text generation holds significant application value in areas like dialogue systems, automatic summarization, and machine translation.