Text translation and summarisation using Natural language processing

Natural Language Processing (NLP) plays a crucial role in text translation and summarization. In translation, NLP algorithms analyze the structure and meaning of a source language, converting it into a target language while preserving context and nuances. This involves techniques like tokenization, part-of-speech tagging, and semantic analysis. For summarization, NLP can condense large volumes of text into concise summaries, capturing key points and main ideas. There are two main approaches: extractive summarization, which selects important sentences from the original text, and abstractive summarization, which generates new sentences that convey the essence of the text. Together, these NLP applications enhance communication and information accessibility across languages and contexts.