Text summarization is the process of reducing the amount of text in a document into a simple, shorter version that only contains the essential components of the original document. As human information increases every day with a high magnitude, processing this data manually becomes increasingly difficult. That’s where text summarization comes into play as it can reduce the reading time, accelerate the process of research, improve learning and more.
Types of Text Summarization
There are mainly two types of text summarization techniques i.e., Extraction-Based Summarization and Abstraction-Based Summarization.
Extraction-Based Summarization
The extractive method involves selecting vital parts of a text and stringing them together to create a summary. This method does not generate any new sentences; it merely merges important sections of the source document. This is why the outcome is readable and factually correct, even though there may be some abrupt transitions.
Abstraction-Based Summarization
This approach involves generating new sentences from the original text. This approach can produce a more coherent and fluent summary as the sentences are generated based on the understanding of the text, similar to how a human would rewrite a passage. However, this particular approach is more complex and requires deeper Natural Language Processing (NLP).
Importance of Text Summarization
In this era of the information explosion, we are overloaded with data. To cope up with this surplus data, text summarization is essential. It helps condense data while preserving information to make it easier to read, especially for busy individuals.
It is beneficial for various industries, such as journalism for drafting headlines, businesses for summarizing customer reviews, and legal advice for summarizing legal texts, and more.
Challenges in Text Summarization
Text summarization comes with its set of challenges. A summarized text must deliberate the outlined plot, which is challenging while maintaining quality. Biases can creep into summaries, especially when contexts and sentiments are inferred. Further, maintaining the coherence of the summed-up text, especially while using the extraction summarization technique, can be challenging.
The Role of Artificial Intelligence in Text Summarization
AI plays a significant role in the text summarization technique. Natural Language Processing (NLP), a branch of Artificial Intelligence, aids in understanding and analyzing the human language. Machine Learning, a part of AI, provides algorithms to summarize the text and predict the results. Deep Learning, a part of Machine Learning, helps in abstraction-based summarization by generating new sentences.
Text summarization has become an important part of modern information processing. It helps in dealing with bulky data, thus saving time and making the data more understandable. While there are certain challenges like maintaining the consistency and minimizing biases, advancements in AI, Machine Learning, and NLP can help overcome such challenges.
1. What is Text Summarization?
Text summarization is a process of creating a concise and coherent version of lengthy documents by extracting the essential information.
2. What are the types of Text Summarization?
There are two types of text summarization techniques, extraction-based summarization and abstraction-based summarization.
3. What is the importance of Text Summarization?
Text Summarization is essential to deal with surplus data in the information blast era. It condenses the data for easier readability, which is helpful across various sectors.
4. What challenges does Text Summarization pose?
The challenges of Text Summarization mainly involve maintaining the consistency of the text, keeping the biases to a minimum, and retaining the outlined plot’s original feel.
5. How is AI aiding Text Summarization?
AI aids Text Summarization through NLP, Machine Learning, and Deep Learning. It helps analyze the human language, provides algorithms for summarization, and assists in generating new sentences.
