If you browse the internet for a while, you will notice the immense amounts of data available. This overload of information has affected users’ attention spans worldwide. Therefore, it is important for us to make our content as concise and easy to digest as possible.
Thankfully, we don’t have to do everything manually ourselves these days. This is because of the modern and robust Natural Language Processing models (NLP models) used in various tools. In this article, I will share methods and techniques to effectively summarise long writings.
But before we get into the specifics, let’s define summarisation.
What Does it Mean to Summarise Long Text?
During this process, the original meaning and the important points of the text must be kept intact. So, in essence, summarising text means shortening the length of a write-up while preserving the original meaning.
Uses of Summarizing
When we think of the use cases of condensing information, we can think of a few things. However, to put things in perspective, here are some uses of this technique. These will help you understand the importance of this process.
- Students can use summarisation to understand longer texts when they are short on time.
- Professionals who have to deal with immense amounts of data can summarise it into shorter versions to understand it better.
- Content writers can use summarisation to repurpose their work (blog social media captions).
- Changing content to fit the requirements of various audiences.
These are some uses of summarisation that show how practically applicable this skill can be. Now, let’s see how NLP models allow us to do this.
NLP-Based Summarizing
Now that you understand summarising as a concept, you might be thinking where NLP comes into play. The added value that NLP provides is that it deeply and accurately understands things like context, language patterns, or the underlying meanings of sentences.
Two main types of summarisation are used in NLP models:
- Abstractive Summarisation: During this, the model understands the text as a whole and summarises using new sentences that represent the same idea.
- Extractive Summarisation: In this summarisation, the old sentences are modified to be shorter. This also gets the job done, but there might not be a coherent flow of information as there would be in abstractive summarisation.
Overview of Summarization Process in NLP Models
Every model could have a few things that are unique to it, but the core process used by these tools is as follows:
- First of all, the model converts the input into as readable a format as it can.
- Next, it understands the whole input information. In this process, it might identify the topic, keywords, sentence placement, etc.
- After that, the model creates a summary. In extractive models, the most important lines are chosen. In abstractive models, new and summarised lines are generated.
- Then, the final output is formatted and presented to the user.
How to Summarise Text Using an NLP Model?
You can summarise text using different methods depending on your skills and needs. Let’s explore them in detail:
Using an AI Summarizing Tool
This is the easiest option. AI text summarisers use NLP models behind the scenes. You simply paste your long text, click a button, and get a summary in seconds. AI Summarizer is a popular example that uses advanced NLP models to condense lengthy text pieces into clear summaries. It also comes with a summary length adjuster that allows users to limit the length of the output summary.
Using Pretrained NLP Models
If you have some technical knowledge, you can use online platforms like Hugging Face. They offer ready-made models that can summarise text with a single function call. You do not need to train the model yourself.
For example, the Transformer library lets you load a summarisation model and run it on your text. This is great for developers or students working on AI-related projects.
Related Readings: Hugging Face – Revolutionizing NLP and Beyond
Training Your Own Model
This option requires more time and skill. You can train a custom summarisation model using large datasets. This approach gives you more control over style and quality. It is useful for research projects or specialised business tasks.
Final Thoughts
Summarising long text with an NLP model is one of the easiest ways to save time and understand information quickly. Since NLP models can read large amounts of text, it makes things very easy for you. With just a single click, it can produce short, clear summaries that help you focus on what matters. Furthermore, if you are clear about what you want to achieve with a summary and what tools you will use for it, the whole process becomes even simpler. So, follow the instructions shared in this article and apply them to fully utilise the benefits of this technique.
Frequently Asked Questions
How does NLP help in summarising text?
NLP models automatically produce precise, cohesive summaries by comprehending context, keywords, sentence structure, and meaning.
Do I need coding skills to use NLP summarisation?
Not always. No-code AI summarising tools are available. You only need to know how to code if you want to create your own models or use pretrained ones.
Can NLP summarisation change the meaning of the original text?
While abstractive summarisation may slightly reword concepts, good models attempt to maintain meaning. Check summaries for accuracy at all times.
Next Task: Enhance Your AI/ML Skills
K21 Academy provides expert training, hands-on labs, and practical insights to help your team master AI and machine learning cloud platforms, turning your AI ambitions into reality. Explore the power of generative AI applications and advanced analytics today!
Ready to master AI, machine learning, generative AI & Agentic AI? Join K21 Academy’s AIML FREE class and take the first step toward a $250K+ career in AI, ML, Data Science, GenAI & Agentic AI—even without coding experience! Secure your spot now!
