Using AI to Simplify and Streamline Text Summarization

The purpose of a summary is to quickly give the reader or listener an idea of what the material is saying, however, summaries are nearly never provided when you need them most. What if you could save a huge chunk of the time you spend reading full documents and ask specific questions related to a document? With text-generated AI summarization powered by Natural Language Processing (NLP), you can. Text summaries are becoming more useful to extract the most important details from articles, blogs, lectures, website pages, stories, presentations, and more.

How can Natural Language Processing and AI help me with text summarization?

Extractive text summarization

Extractive summarization works well with factual documents and focuses on extracting the most important existing snippets of text in a document. Natural Language Processing (NLP), finds the most important sentences based on a few factors, like how often words show up, how often phrases appear, how regularly similar sentences pop up, and several more. The model then assigns weights to these sentences and can either return the sentences in weighted order or the same order it was in from the text.

Benefits of extractive summarization:

• Well suited for business needs such as fact sheets and other documents
• Not very dependent on training data
• Faster and more processing power-efficient than abstractive

Disadvantages of abstractive summarization: 

• Does not care nor does it know what the text actually means. 
• New sentences are not created

Extractive text summarization

Abstractive summarization works well with documents where sentences may build upon each other and you don’t need the specific facts. An abstractive summary tries to obtain meaning from the document and use that to produce a summary. AI used to create abstractive summaries don’t actually know what the text means. The models create mathematical representations of the text and then compare those representations to data that the AI was trained on. Once the AI has created those representations, it derives other representations of the data and puts those together to create and return sentences.

Benefits of abstractive summarization: 

• The ability to guess at the “meaning” behind the text
• The ability to combine multiple sentences into one
• Works well when you don’t need the specific facts of a document

Disadvantages of abstractive summarization: 

• Dependent on the text the model was trained on
• Not a good fit for producing objective summaries

Now you understand the benefits of each type of summarization. Soon, we will release a second blog to teach you how to use Mantium to summarize a blog post. Stay tuned!

Are you new to Mantium?

Join us on the journey to democratize AI and experience all that Mantium can offer, including:

• AI templates – Pre-populated templates give you the ease of use you desire and a path to AI in less time than it takes to drink a cup of coffee. 
• Quick and efficient testing – Launch prototypes quickly and share the link to gain team buy-in and collaboration. 
• Dedicated customer success team – As you might already know, we offer a world-class team of data scientists and NLP engineers to support your needs, from start to finish. 
• All levels welcome – from dabbler to the AI enthusiast. Regardless of your AI skill set or level of expertise, we have the people and tools to support you in your AI journey. 

Sign up to join the waitlist for the beta of AI Builder.

Follow us on Twitter @MantiumAI | Follow us on LinkedIn Mantium

ABOUT THE AUTHOR

Laura Scavo-Shin

Mantium

Laura Scavo-Shin is a Content Manager at Mantium, where she oversees developing and curating content for marketing campaigns and activities. Laura enjoys writing and educating customers and prospects on AI and bringing Mantium’s offerings to the masses. Laura holds a Master's of Science in Marketing and a Bachelor's of Arts in Communication. In her free time, she loves traveling with her husband to their favorite beaches in Mexico, the islands of Hawaii, and the Caribbean, riding her Peloton, and loving on her dog Stella Wolf.