SciencePOD

Visual representation of AI's summarising power

The Amazing Summarising Ability of AI

Is AI able to use its summarising ability to extract information?

Artificial intelligence (AI) algorithms are already well established in information businesses.  The  summarising ability of AI can make our lives much easier. AI is most effective when used to augment, rather than replace, human capabilities. 

When it comes to published information, AI can be used to accurately summarise most complex subjects, such as  medical or scientific research papers. When relying on AI for accurate summaries, however, high-quality tools are essential.

Why do we use AI’s summarising ability?

AI has the summarising ability tools are practical on multiple levels, and can greatly increase productivity. AI saves human readers time by going through large, complex documents like scientific studies. The AI identifies which studies are relevant to the reader’s needs, and worth reading in full.

The time saved can then be spent on more specialised  creative tasks, such as reflection or analysis. For journalists writing on complex scientific or medical subjects that require many background resources, this reclaimed time is extremely valuable. AI summarisation can streamline many arduous research activities, making it extremely useful for scientistsmedical experts and people working in other R&D roles, such as industry executives in marketing, communication or medical affairs. Professionals working in the innovation ecosystem also need to stay abreast of the latest research. 

One of the by-products of summarisation is the automated extraction of meaningful keywords from the source document. These can subsequently be recorded in the document’s metadata to enable faster, more efficient retrieval of relevant summaries. 

How can AI’s summarising ability help?

Natural Language Processing

The AI summarising ability that responds to text and spoken words uses Natural Language Processing (NLP). NLP brings together many scientific disciplines, including linguistics, computer science, and AI. NLP is used to program computers to process and analyse large amounts of human language, often in text format.

NLP-based AI determines the most important words, phases and information in a source document, based on the frequency with which they occur, to effectively summarise complex documents. The AI can then create a summary based on these natural language occurrences. AI summarisation combines various language processing approaches, including language compression, to ensure that the final summary captures the essence of the source document concisely. 

Extractive summarisation

One model of NLP specialises in extractive summarisation. The algorithm identifies the most prevalent information using a statistical analysis of the full text and selects the most meaningful sentences and words according to set parameters. It uses this analysis to create a short-form summary of the longer text, composed of the extracted sections. 

Abstractive AI Summarisation

Another approach is called abstractive summarisation. This kind of AI summarisation also analyses a text to create a concise summary. Instead of simply pulling and reproducing key sections verbatim, it uses its analysis to create novel sentences and rewordings. To date, this approach has mainly been used to create very short summaries of longer works.

Machine Learning Solutions

Other forms of AI are also being used to generate automated summaries.

Machine learning creates predictions based on the most prevalent elements of a source text, referring to a large learning corpus of existing texts. The machine is taught how to summarise texts according to a set of parameters, using its reference materials, and continues to learn iteratively from every new text source it analyses. 

Machine learning  has been applied to a number of interesting text-generation problems: in one experiment, for example, a computer was asked to write new poetry based on a historical collection of human-written examples.

Machine learning can fine-tune the results obtained by extractive summarisation, but it’s rarely the go-to method for such AI summarisation.

The future of AI’s summarising ability

The summarising ability of AI, for all its imperfections, presents many possibilities for scientific research. As algorithms are refined, the accuracy of such tools will increase and  source materials will be selected and analysed in more granular ways.

At the time of writing, AI summarisation is already boosting the speed with which we can access complex information and improving the ways in which we communicate key data.

Looking for a useful AI summariser? Check out SciencePOD’s AI Newswire.