SciencePOD

AI Summarisation

AI Summarisation Proves to Be a New Productivity Tool

How can AI summarisation speed up access to scientific literature?

AI summarisation tools are one way to cut through the vast amounts of scientific studies in search of relevant knowledge. These tools are incredibly sophisticated and play an increasing role in research. 

As science and medicine become increasingly specialised, there is more variety of distinct information available.  There are and estimates 2.5 million new scientific studies published each year, according to the STM Association, making it hard to stay abreast of every fresh developments. As an illustration, at the time of writing there were more than 6,400 COVID-19-related clinical studies published or pending.

Why use an AI summariser?

The obvious answer is to save time. An AI summariser tool displays the key points from a study in a concise and accurate manner, allowing a busy scientist to skim more studies at greater speed.

An AI summariser tool provides the main details of each study, allowing subject matter experts to swiftly identify which papersthey should read in detail. AI summarisers also present sources clearly, enabling researchers to identify and examine new resources relevant to their work.

Keeping up to date

AI summarisation can be used to help monitor the latest news. Because of the conciseness of summaries, lots of content can be processed to the most integral factors quickly. Thus, the latest news from multiple sources can be condensed and found in one location. 

This time-saving tool allows you to keep track of the latest developments within the science and medical community. It can also alert you to superseded research, so you are always working with the best information. 

How does AI summarisation work?

The most effective AI summarisation techniques rely on Natural Language Processing (NLP). This field combines a range of computational techniques to analyse and represent human language. NLP algorithms combine rule-based design and statistical models to identify the most relevant parts of a source text.

In extractive summarisation, the algorithms rely on statistical analysis to identify key linguistic features and omit unnecessary words. The data passes through a ranking step, which extracts the most relevant words to determine the text’s deep meaning. 

There are a number of summarisation tools available, so it’s worth spending some time to research the one that best meets your needs. SciencePOD’s AI Newswire tool is a rigorous, reliable and effective choice, trusted by the scientific and medical research community.