Summarising Messy Data

One of the challenges in data-to-text NLG is creating good summaries and insights when the input is flawed (incomplete, incorrect, or inconsistent). One of my PhD students has been working on this problem, and it is a hard one! But a good solution would be hugely valuable for society. I may be able to offer a PhD studentship in this area, contact me if interested.

building NLG systems

Challenges are Same for Neural and Rule NLG

The fundamental challenges of building useful data-to-text NLG systems are the same regardless of whether we build systems with rules or transformers. We need to understand where NLG is useful, choose good content to communicate, robustly deal with edge cases, allow users to configure and control the system, and evaluate properly. I’d like to see more research on these fundamental issues, regardless of technology used.