Generative AI for Content Lifecycles
Artificial Intelligence (AI) in general and Generative AI (GenAI) in particular are transforming the entire content lifecycle, impacting each step in the digital content value chain. They boost efficiency and enable new approaches to content production, distribution and analysis that are data-driven and explainable.
What is Generative AI?
GenAI is a branch of AI focused on creating new content learning patterns from large datasets. This includes text, images, videos, and audio. It utilizes Large Language Models (LLMs) to generate outputs by learning and mimicking patterns found in the training data. Traditional AI uses supervised learning to perform specific tasks, such as prediction and automation. Generative AI, by contrast, often employs unsupervised or semi-supervised learning to generate new content instead of annotating or classifying existing content.
Major Steps in the Content Lifecycle
webLyzard’s experience in AI research goes back more than fifteen years, with a focus on extracting factual and affective knowledge. Automated methods allow us to analyze vast amounts of textual data from online media to extract actionable insights. This includes public sentiment or emotions toward an issue or brand, for example, and how a campaign is affecting the perception of citizens and stakeholders. Evolving knowledge graphs help to structure these insights. Named entity recognition and linking components map them to persons, organizations and locations mentioned in the online coverage. Building on these analytic capabilities, webLyzard supports both the production and the analysis of content.
Production: Text Authoring and Optimization
Our strategic partner Storypact offers a context-aware text editor that uses webLyzard metadata to guide its GenAI-driven recommendations. The tool redefines how writers use AI to tell stories and shape their narratives by combining data-driven methods with human ingenuity and intuition. Storypact will soon launch its tool to the public. We invite you to subscribe to our newsletter to obtain a free test account and be among its first users.
Analysis: Distilling Search Results
Extracting common threads from hundreds of different online documents can be time-consuming, even with the help of story detection mechanisms that cluster search results. That’s why webLyzard dashboards allow you to summarize search results with Generative AI. This is ideal for just browsing or generating a summary as a first step that guides more in-depth research. The LITE version of the dashboard features a new tab that translates each selected topic or metadata attribute into a bullet, as shown below. The PRO version offers a similar view in its content area. Also, it includes an extended tooltip to summarize a specific aspect of the coverage related to the clicked keyword, source, person, or location.
Next Milestone of our GenAI Journey
The EU-funded research project MultiPoD will build a collaboration platform to support public deliberation and democratic processes. A transdisciplinary expert consortium led by webLyzard technology will use GenAI to improve the political dialogue at a European level. Combining a Culture-Specific Language Model (CSLM) with an evolving Culture-Specific Knowledge Graph (CSKG) will enable political deliberation across linguistic and national barriers.
GenAI algorithms for knowledge extraction, representation, and content creation are constantly evolving. They enable efficient tools for visual analytics, search result distillation, and content creation. Such tools need to be accurate and scalable enough to be used for both research and commercial applications.
