Besides Machine Translation, name another NLP task that benefits from the Transformer architecture.

Text summarization is another NLP task that significantly benefits from the Transformer architecture. Text summarization involves condensing a longer piece of text into a shorter, more concise version that retains the most important information. The Transformer's self-attention mechanism allows the model to effectively capture long-range dependencies and relationships between words in the input text, which is crucial for identifying the key information to include in the summary. Both abstractive and extractive summarization techniques leverage the Transformer architecture. Abstractive summarization involves generating new sentences that convey the meaning of the original text, while extractive summarization involves selecting and combining existing sentences from the original text. Models like BART and T5, which are based on the Transformer architecture, have achieved state-of-the-art results on various text summarization benchmarks. The Transformer's ability to understand the context of the input text and generate fluent and coherent output makes it well-suited for this task. For instance, given a lengthy news article, a Transformer-based summarization model can generate a short, informative summary that captures the main points of the article without simply copying sentences verbatim.