Using AI to Simplify and Streamline Text Summarization

By Laura Scavo-Shin

August 3, 2022   ·   4 min read

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?

There are two types of summarization when it comes to using AI, extractive summaries and abstractive summaries. Let’s explore these two types of summaries.

Extractive summarization extracts words and word phrases from the original text to create a summary.

Abstractive summarization learns and internal language representation to generate more human-like summaries, paraphrasing the intent of the original text. 

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!

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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.

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