A Survey on Text Summarization Techniques
This paper delves into the fascinating realm of text summarization, a process that distills lengthy content into shorter, more manageable versions, like a shortcut for understanding lengthy documents without reading every word.
Abstract
- In today's digital era, the ever-expanding volume of textual data, abundant on the internet and across various repositories, poses a formidable challenge for manual processing. This paper delves into the fascinating realm of text summarization, a process that distills lengthy content into shorter, more manageable versions. It's like a shortcut for understanding lengthy documents without reading every word. Text summarization refers to creating shorter versions or summaries of lengthy text while maintaining its core idea. This system finds diverse applications, from generating search engine snippets to condensing news headlines, facilitating lawsuit abstraction, and summarizing complex biomedical and clinical texts. Text summarization, particularly for extensive textual documents, presents a significant challenge in the field of natural language processing (NLP). It plays a vital role in NLP by using software to condense lengthy documents into concise summaries that capture the key points. This paper will also introduce different approaches to text summarization, and we will touch on some modern techniques and models that make it all possible.