Abstractive summarisation is a form of automated text generation that turns huge blocks of text into easily consumable summaries.

If content is key to your daily operations, you could benefit from this technology.

Read on to learn more about the scope of abstractive summarisation, what benefits it provides, and how might it help your company to grow.

What is abstractive summarisation

Hundreds of years ago, if you wanted to learn about a subject, you read a large leather-bound book or took on an apprenticeship. Knowledge was for the few, not the many, and it was not easily obtained. In the digital age, the entire works of man are just a query and click away, and most of it has been condensed into easily consumable videos, slideshows, and texts.

The modern world moves at a tremendous pace. People do not have time to read epic tomes and watch hour-long videos. That’s where abstractive summarisation comes in.

Abstractive summarisation technology cherry-picks the most relevant points from a data set and creates an easily digestible summary. Most texts can all be summarised in this manner, allowing you to consume huge amounts of data without expending equally large amounts of your time.

This unique form of summarisation uses natural language processing enrichment to summarise text that didn’t appear in the original content. In other words, while extraction-based summarisation methods take pieces of the source material and reuse them verbatim, concise summary generation creates its own unique summary while maintaining the semantic representation.

As opposed to simply copying, the abstractive methods take the input sequence of text and create a concise summary that is succinct and easy to understand.

Which industries can benefit from abstractive summarisation

There are many ways that companies can benefit from automated text generation solutions. If you use content to monitor trends, acquire customers, or market your products and services, then abstractive text summarisation services can help.

Here are a few of the ways that these services can be used:

Media and trend monitoring

Over 7.5 million blog posts are published every day on blogging platforms and personal websites. It is an extraordinary amount of content, and if you are looking for the latest must-read stories and trends, it makes for a pretty overwhelming experience.

Abstractive summarisation can help with this. What’s more, as it uses natural language generation, the summaries are natural and easy to read, as opposed to simply listing a few awkwardly worded highlights.

SEO and Content Marketing

SEO is at the heart of every online business. Whether you are promoting digital services, selling a product, or growing a personal brand, SEO will play a key role in your marketing plans.

In the distant past, SEO was all about keyword spamming. Search engines gave a lot of weight to the density of keywords and keyword domains, and even when that era faded away, there was still a disproportionate focus on easily targeted words and phrases.

These days, the quality and relevancy of content is the most important consideration. By using abstractive summarisation, you can understand what your competitors are doing and what’s working for them. Once you have that data, you can work on improving your own rankings.

Content marketing, on the other hand, is one of the many tricks employed by content marketers to create a large piece of content, such as a blog post or guide, and then break it up into many smaller pieces. These segments can then be posted to blogging platforms, shared on social media, and even added to videos and images for more eye-catching content.

Creating those snippets can be a challenge, and if you are just spending hours writing a guide, the last thing you want to do is copy, paste, paraphrase, and edit your way toward dozens or hundreds of additional posts.

With abstractive summarisation, you do not need to. It can summarise your long-form content and create shareable segments for sites like Facebook, and LinkedIn.

You can even use automated text generation to create video scripts. Just summarise your longer content and then give those summaries to a writer or editor who can draft them into scripts for YouTube, TikTok, or your website.

In-house meetings

How many virtual meetings do you attend every week? How many hours do you spend talking about things that could have been expressed just as easily in an email? You’re not alone, as it is an issue plaguing many companies that work in the digital space.

If you use voice-to-text programs to transcribe those meetings, abstractive summarisation technology can take input text and then highlight the key points.

These highlights can be passed around to everyone in your organisation and used as a reference for future work. It means you do not need to repeat meetings just because a staff member wasn’t there the first time or needs clarification on some of the points you discussed.

Scientific papers

In the academic world, scholars and researchers often have to go through hundreds of scientific papers to stay updated with the current state of their field. These papers can be incredibly long, complicated, and filled with jargon. Reading each one carefully could take hours, and sometimes even days.

Abstractive summarisation can make this process more efficient. Instead of spending countless hours going through these papers, one can use abstractive summarisation algorithms to extract the key points and methodologies, summarizing them in a way that is easier to understand and more accessible. This not only saves time but also makes it easier to compare the outcomes and approaches of various studies.

Moreover, researchers can use summarisation tools to create highly condensed versions of their own research for dissemination in non-academic settings, thus making scientific knowledge more accessible to the public. Summarised versions can be used for press releases, social media sharing, or even grant applications where conveying the essence of the research quickly is crucial.

Reinforce learning

Reinforcement learning (RL) algorithms often generate a lot of data during their training process. This includes logs, metrics, and performance benchmarks that detail how well the algorithm is doing at any given task. This data is essential for fine-tuning the model and understanding its limitations, but it can be overwhelming to sift through.

Abstractive summarisation can play a role here by condensing the voluminous logs into actionable insights. For instance, it can summarise the performance metrics over different stages of training to highlight where the model is excelling or lagging. This enables researchers and engineers to focus on the most relevant aspects of the training data, thereby streamlining the iterative process of model development.

Moreover, abstractive summarisation can be used to translate complex RL findings into layman’s terms. This can be extremely beneficial for interdisciplinary teams where not everyone may have a deep understanding of reinforcement learning. The technology can also be used to communicate the progress and findings of RL projects to stakeholders in a straightforward and accessible manner.

How accurate is abstractive text summarisation

The accuracy of abstractive summarisation can vary depending on several factors, including the quality of the source material, the algorithms used, and the specific requirements of the task. With Identrics’ Abstractive Summarisation, you can put those concerns to rest.

Here’s why:

Fact-Checking Algorithms

One of the unique features of Identrics’ solution is its fact-checking algorithms. These algorithms act as a layer of quality control, ensuring that the summaries generated are not only concise but also accurate. By verifying the information against the original text, these algorithms significantly reduce the likelihood of “hallucination errors,” where the summary includes details not present in the source.

Data-Driven Summaries

The machine learning model employed by Identrics is data-driven, meaning it is trained on a large set of text data to understand the context and semantics deeply. This approach helps the model avoid “omission errors” by ensuring that all the key points from the original text are covered in the summary without losing any crucial data.

Natural Language Processing Enrichment

By leveraging advanced Natural Language Processing (NLP) techniques, Identrics’ tool ensures that the semantic essence of the original text is retained. This addresses the issue of summaries that might be too simplistic or miss nuances, enhancing both the depth and reliability of the generated summaries.

Addressing accuracy with Identrics’ Abstractive Summarisation

By focusing on these key areas, Identrics’ Abstractive Summarisation technology offers a reliable and accurate tool that meets the needs of various industries and applications, from trend monitoring to scientific research.

Our model is able to produce short, data-driven summaries that cover all of the key points in the original text, without losing any important data.

Abstractive summarisation is just one of the solutions offered by Identrics.

We create solutions that are geared toward making your life easier and your business operations smoother. Our technological services make it easier to find, digest, organise, and create content, ensuring your business is ready for the modern content-hungry marketplace. Contact us to discuss more about our latest projects.