Importance of Text Summarization
Our lives are surrounded by a large amount of information. When it comes to our day with daily information flow comes articles, news, blogs, tries, social media posts, and scientific papers. It is one big amount of useful information to understand something and develop a decision based on the information you need to have insights or process it. However, no human can eat that much information during his life. Here what stands up behind the priority of Text Summarization.
Example: Suppose a company would like to examine how their product is performing according to customer reviews. Going through every one of the thousands of reviews manually can be extremely time-consuming. This is where Text Summarization comes in – it can go through all the reviews in no time, sum up items that repeated more common complaints or praise itself, comments, and points to focus on to improve the product.
Text Summarization can be used in many other fields. It summarises long stories into short descriptions or summarize multiple original source summaries in literature reviews. Text summarization is very helpful , where people always deal with so much information daily.
Text Summarization Techniques
Despite its manual-to-automated evolution facilitated by AI and ML progress, Text Summarization remains complex. Text Summarization is critical in news, document organization, and web exploration, increasing data usage and bettering decision-making. It enhances the comprehension of crucial information and the value of the text. Combining syntax and semantics, it creates clear, highly coherent summaries, which define people’s connection with information.
In this article, we are going to explore the importance of text summarization and discuss techniques like extractive and abstractive summarization.
Table of Content
- Importance of Text Summarization
- Text Summarization Techniques
- Extractive Summarization
- Techniques used in Extractive Summarization
- 1. Statistical Approaches:
- 2. Graph-Based Methods:
- 3. Machine Learning Algorithms:
- 4. Sentence Scoring:
- Abstractive Summarization
- Techniques Used in Abstractive Summarization
- 1. Sequence-to-Sequence Models:
- 2. Attention Mechanisms:
- 3. Pre-trained Language Models:
- Hybrid Methods
- Conclusion