What is Text Summarization?
Text summarization can be broadly classified into two types:
- Extractive Summarization: This method involves selecting important sentences or phrases directly from the source document to create a summary.
- Abstractive Summarization: This method generates a summary by understanding the content and rephrasing it, often using new sentences that are not present in the source document.
Abstractive summarization, which mimics human-generated summaries, is more challenging but also more powerful. It requires a deep understanding of the context and semantics of the text.
Text Summarizations using HuggingFace Model
Text summarization is a crucial task in natural language processing (NLP) that involves generating concise and coherent summaries from longer text documents. This task has numerous applications, such as creating summaries for news articles, research papers, and long-form content, making it easier for readers to grasp the main points quickly. With the advancement of deep learning, transformers, and pre-trained language models, text summarization has become more efficient and accurate. Hugging Face, a leader in NLP provides state-of-the-art models that facilitate text summarization tasks.
Contact Us