Have you ever heard of the expression tl;dr? It stands for “Too long, didn’t read”. It likely originated on the comedy forum Something Awful around 2002, and spread to other online forums. Nowadays it is used by authors when they give a short overview of their text. Does it sound like something you might have said before too?
There are a lot of good examples for when you prefer to have a short summary of the document for instance if you are not sure yet if it is of interest for you or when you actually only need the essence of it to continue with your work.
A good example for that might be the book “On the Origin of Species” by Charles Darwin. Most likely all of us know his famous ideas on evolution on natural selection but only a handful of people have bothered to read that 502 pages long book. Simply because for us it has not been necessary to understand all the details but the main messages.
The number of published papers and patents grows exponentially. About 2 million papers are published per year, that’s about 5,500 per day. And the growth is growing! So, to stay on top of all the new findings one has to navigate through a vast amount of literature. And after a long search for the right papers, one then has to read each paper and summarise it or at least understand the main takeaways. Instead of reading through lengthy papers; Wouldn’t you prefer to quickly understand the main messages of the paper and if it is interesting of course, go ahead and read it as your night time lecture?
If we can quickly assess the main findings and messages of one or a couple of similar papers, patents and internal documents, we would use less of our valuable time reading and summarizing papers. Hence, we can allocate our resources to more important tasks like using the empirical findings on inventing a new product or drug.
Iris.ai Summarization Tool
Our Summary Tool, as the name indicates, automatically produces summaries of any given document for you. In general, there are two types of summaries – extractive and abstractive. With an extractive summary one identifies the relevant information that is then extracted and grouped together to form a concise summary. In contrast to that, the Iris.ai Summary Tool creates abstractive summaries, meaning that the machine rewrites the entire document by building internal semantic representation, and then a summary is created using natural language processing.
The tool can make a summary of one as well as multiple documents. For the multi documents the machine identifies the parts of text in common and only makes a summary of that. Additionally, the summary can be based on abstract only or full text. The user can also adjust the length of the summary. We can take this feature a step further – for example, by you providing a topic of focus and the machine summarizing only text related to that, not the full texts. Let’s say you want a summary of only the experiments, or only the methods, or only one of the topics covered. This is still experimental but very exciting.
The tool can be integrated with your systems and the user interface can be customized.
Watch the full demo alongside with the peak behind the curtain of the technology we’re using here: