What is an AI Summariser? Is It Any Good? Should You Use It?
Editor / February 6, 2025
An AI summariser writes a summary of texts using artificial intelligence technologies. There are many AI-powered summarisers you can use for free too.
I’ll discuss the pros and cons of this tool in this FastLinky blog post to help you decide whether to use this tool or not.
This discussion will provide you with satisfactory answers to the three questions in the title of this post.
What is an AI Summariser?
AI Summarisers are tools that produce a summary of the key ideas in a written document. They can summarise sentences, paragraphs or an entire long-form write-up.
How to Use a Summariser?
Using the latest summarisers powered by artificial intelligence (AI) technology is one of the easiest jobs on earth: you paste a text in the input box, hit the ‘summarise’ button and the summary will appear in the output box. done.
What are the Best Summarisers?
QuillBot, Scribbr, Grammarly, ZeroGPT and Ahrefs are some of the most popular AI text summarising tools.
All of them have a free version but with limited functionalities.
How Does a Summariser Work?
Though you can get your summary in a jiffy, a summariser uses some highly complicated generative AI technologies to give you that summary.
AI summarisers may use two methods to summarise: extractive method and abstractive method.
Extractive Method
In this method, summarisers assess the importance of sentences in the context of the text.
They then identify and extract the key words and phrases in those sentences and compile a summary using them verbatim, without paraphrasing the sentences.
Abstractive Method
In this technique, a summariser first understands the meaning and context of a text. It then generates new phrases and sentences to distill the basic idea of the text and produce a summary.
This process is more advanced and produces summaries that read more natural than summaries produced by the extractive method.
But in both cases, summarisers use highly complicated algorithms powered by some marvellous artificial intelligence techniques.
I’ll now briefly discuss these techniques for your reading pleasure.
Generative AI
Generative artificial intelligence is a special type of AI that can create new content like texts, sentences, images or videos.
It can learn complex subject matter like art, culture, language etc and use this learning to produce new content and improve its efficiency.
Machine Learning
A type of AI that enables machines to learn and improve performance from data without much programming.
A summariser uses machine learning algorithms to identify key elements and structural patterns in a content and produce a condensed version of that content.
Deep Learning
Deep learning is an advanced type of machine learning that uses artificial neural networks to scan and evaluate data.
AI summarisers use this model to produce more accurate results.
Seq2Seq Model
Sequence-to-sequence (Sec2Sec) is a machine learning model that enables a machine to convert sequences from one domain to another. For example, translating English sentences into French sentences.
This model is used in AI to help machines perform tasks like speech recognition and content summarisation.
Transformer Model
A deep learning component, a transformer is an artificial neural network that enables machines to understand relations between components in sequential structures and use this understanding to produce output data from input data.
For example, an AI summariser powered by this model may understand the contextual relationships between ‘grass’ and ‘green’ and produce the sentence ‘the grass is green’.
BERT Model
BERT stands for bidirectional Encoder Representations from Transformers. This technique uses transformers to understand a word in relation to its right and left contexts.
Natural Language Processing (NLP)
This is a field of AI that involves helping machines understand and mimic human language. This is a key technology that powers the AI text summarisers.
Training Data:
Training data is a bundle of information fed into a machine so it can process data, make sense of it and make decisions based on it.
A summariser’s performance largely depends on the quality of training data it refers to while processing data and making predictions or decisions.
Large Language Model (LLM)
Referring to the huge amounts of training data, an AI-powered tool like a summariser can process, understand and produce human language. This process is based on LLM.
Key Benefits of an AI Summariser
An AI summariser can serve the following purposes. Each of these benefits is a curse in disguise, so say the naysayers.
I’m listing some of the key benefits of using this tool side-by-side the criticisms each of these benefits draws.
1. It Helps Comprehension
The Benefit
By providing a gist of a complex document, a summarisation tool can help users comprehend large complex content.
The Criticism
It kills a man’s natural ability to process complex data.
2. It Saves Time
The Benefit
It saves a lot of time. A reader saves time by quickly reading the summary of a large complex document.
It also helps writers quickly comprehend complex content and create fresh content based on the basic idea of the input text.
The Criticism
Apart from killing a man’s natural ability to process complex content, it also encourages content thieves to steal other people’s intellectual properties.
3. Facilitates Quick Decision Making
Using a summariser, businesses can prepare summaries of complex data, for example, market research reports, and make vital business decisions quickly.
The Criticism
A summariser will turn a sharp intelligent executive into a lazy stupid worthless liability as he will prefer a machine to his own abilities.
Besides, AI summarisers often produce summaries packed with erroneous information. Decisions based on such summaries will invite disastrous consequences.
4. It Saves Money
The Benefit
It saves money by summarising large complex content quickly and helping businesses to cut down overheads.
The Criticism
As a result, some honest, efficient and intelligent people will lose the jobs they deserve and need.
Some Key Use Cases of a Summariser
Here my job is easier. I’ll list the benefits only.
All of these uses invites a common criticism: AI summarisers frequently produce biased summaries replete with incorrect information. This makes each of the following uses of these tools potentially dangerous.
- 1. It provides key points of large complex data in a short easy-reading form that helps business executives understand industry trends and competitors’ strategies.
- It helps legal practitioners quickly comprehend complex legal documents and make decisions based on that understanding.
- Financial analysts can use a summariser and get the key points of large and complex financial reports for a quick comprehension.
- It helps marketing professionals get the gist of complex market research reports and make decisions quickly, without wasting time on reading long complicated documents.
AI Summarisers: Ethical Concerns and Risks
Generative AI, the technology that empowers an AI summariser, comes under heavy criticisms on practical as well as ethical grounds.
So far we have seen the bright side of these tools, with some whimpering criticism we haven’t taken much notice of.
Maybe we have made a mistake.
Maybe we should pay heed to the following allegations levelled against these tools.
Summaries Lack Creativity
AI summarisers work on previously-fed data. They can provide a concise summary, but the summary will have a mechanical smell about it and will lack creative touches that only a human being can provide.
Ethical Concerns
An AI summariser has several issues from an ethical standpoint. Some of them are listed below.
It Triggers Unemployment
The wholesale use of summarisers powered by generative AI will make many employees redundant.
Many skilled and efficient employees will see their jobs terminated suddenly, which will bear dangerous consequences on their lives–both family and personal.
It May Spread Biased Content
While making AI summarisers, developers can intentionally or unintentionally put their personal biases in the training data which may reflect in the summaries they produce and hurt some people.
Infringes on Privacy
Many AI summarisers gather their users’ personal information to better their performance. From the machine’s standpoint this may be a grand idea for ‘self-improvement’, but an user may interpret it as a clear infringement on his privacy.
Dangerous Incorrect Summaries
Summaries produced by an AI summariser often contain incorrect information. This may have dangerous consequences, like–
- A doctor treating a patient based on the erroneous summary of his case history may kill the patient with wrong treatment.
- A judge may send an innocent man to jail if his judgement is based on the incorrect summary of the legal documents related to the case.
- A business executive may ruin the prospects of his company by making wrong decisions based on incorrect summaries of market research reports.
Plenty of such examples can be offered to describe how dangerous dependence on machines can be.
It May Thwart Cognitive Development
A serious allegation levelled against generative AI and tools that are powered by it like a summariser is that it may devastate cognitive development.
Students using AI summarisers will not use their brains, but let the machine do their jobs for them.
This may harm their cognitive development gravely.
It Makes People Lazy and Inefficient
Getting the job done by a machine and using the saved time fooling around may sound like quite an idea to some people, including impressionable adolescents and youths.
Over time, this dependence will impair their ability to read and understand complex content. As a result, instead of some strong intelligent citizens, a country will get a bunch of lazy, worthless people.
These machines may even make an intelligent and efficient man stupid if he starts depending on these tools too much.
It may Encourage Thieves
Dishonest people may use AI-driven tools like summarisers and steal others’ content and pass them off as their own and nobody will be any the wiser.
Over time, such unethical practices will lower the quality of content considerably as a machine-generated fake can never convey the creative fervour of a talented creator that goes into his creations.
Should You Use AI Summarisers?
You may find occasions when the use of AI summarisers can be beneficial. But always remember the following points:
- An AI summariser can never summarise like a human being.
- The output these machines produce are often packed with incorrect information.
- Without human oversight, AI-driven tools can do more harm than good.
- An AI-powered tool may enhance productivity, but it can never become an alternative to human beings.
- One should use these tools very sparingly as they have the potential to encourage the inherent laziness in man and discourage creative thinking.
- Always remember, whenever you are using an AI summariser, you are depriving your brains of exercises they need for better performance.
Conclusion
An AI summariser may enhance productivity by producing a concise gist of a lengthy complex document. But its use should always be guided by human oversight to make it safe.
Indiscriminate and thoughtless use of this tool may make an otherwise intelligent and industrious man a lazy worthless nonentity.
Besides, due to their high margins of errors, such summarisers may trigger dangerous chains of events.
The use of such tools must be sparing, supervised by human intelligence and integrity.
FAQs.
Q. What is an AI summariser?
A. It’s a tool that uses AI technologies to summarise content like texts, images and videos.
Q. Is the use of AI summarisers illegal?
A. No.
Q. Are these summarisers free to use?
A. There are many online summarisers that offer free versions with limited functionality.
Q. Are AI summarisers accurate?
A. Not always they aren’t.