8 Ways News & Content Companies use A.I. to Save Money and Improve UX

    The best way to understand the impact of technology is to understand the specifics, examples of how to actually apply technology to solve current problems. The following are 8 common AI Solutions our clients in Newspapers, Magazines, Content and Digital Media Companies utilize to save money and improve user experience.

With this post it should help you understand how to utilize A.I. to save money by implementing scaleable processes and improve user experience without increasing costs. In order to actually benefit from using AI to increase automation, you will need to have the right data, have enough data, have a methodology that can be defined with data points, and be creative in understanding how to apply or craft solutions for parts of your team’s workflow

Examples of AI Solutions for Newspapers, Magazine & Content Companies

        
1) Generating Summaries
        
    
Offer summaries for your readers at scale by automatically generating summaries of the stories you publish. New summary models, such as the abstractive summary model currently utilized by our clients are much more sophisticated than the extractive-based summaries previously available on the market.
      
        
2) Generate Article Titles
     
    
Using the abstractive summarization technology explained above, you can generate a single summary sentence for an article to be used as a title, or in a metadata tag for search engines.
        
        
3) Generate Spoken News (audio) from Text
      
    
Use a generated voice to read the news from text. Customize the voice using training data from a narrator.
       
        
4) Extract Keywords from Content for SEO & SEM Campaigns
       
    
Generating keywords for every article reduces the workload of content writers and editors. Utilizing automatically-generated keywords increases the likelihood that relevant keywords aren’t missed. Bidding on keywords you rank well for increases your quality score.
     
        
5) Classify Content
        
    
We use recommendation systems to build crosslinks to published content, and to suggest relevant articles to users. This automatically saves time and increases user engagement. User level recommendation systems can better target individual users with content that is engaging so they spend more time with you.
     
        
6) Anomaly Detection
       
    
You can automatically classifying news content by section, and customize the label with sections you care about. May be more valuable to a content aggregator
        
        
7) Semantic Search
     
    
Most newspapers and file systems search engines use exact text matching, and if you don’t have the exact query, you will likely miss a great deal of relevant content. Improve your writers’ ability to find source material within your own database by enabling your own people to search with natural language; while also enabling your readers to successfully search and find more relevant content.

Example of queries:

Semantic Search: “European Union Response to the Pandemic.”  would include results that would require you to input an

Exact Text (elastic search): “Spain response to covid-19” + “Spain response to coronavirus” “German public policy response to corona” … 

Understand the difference? See the problem?

8) Save on Licensing Costs for Aggregators

    
Many countries have laws regarding what level of content, even down to a specific amount of words or words you can use in a row, that may be used without your infringing on copyright laws and paying the requisite licensing costs. By utilizing abstractive summarization (E.G. a summarization model that rewrites content) you are producing a derivative work that, with minor edits, can be a compliant derivative work. The result: savings in time and money and better quality results!