Do Text-to-Vis Benchmarks Test Real Use of Visualisations?

Large language models are able to generate code for visualisations in response to user requests. This is a useful application, and an appealing one for NLP research because plots of data provide grounding for language. However, there are relatively few benchmarks, and it is unknown whether those that exist are representative of what people do in practice. This paper aims to answer that question through an empirical study comparing benchmark datasets and code from public repositories. Our findings reveal a substantial gap in datasets, with evaluations not testing the same distribution of chart types, attributes, and the number of actions. The only representative dataset requires modification to become an end-to-end and practical benchmark. This shows that new, more benchmarks are needed to support the development of systems that truly address users' visualisation needs. These observations will guide future data creation, highlighting which features hold genuine significance for users.

Meeting Decision Tracker: Making Meeting Minutes with De-Contextualized Utterances

Meetings are a universal process to make decisions in business and project collaboration. The capability to automatically itemize the decisions in daily meetings allows for extensive tracking of past discussions. To that end, we developed Meeting Decision Tracker, a prototype system to construct decision items comprising decision utterance detector (DUD) and decision utterance rewriter (DUR). We show that DUR makes a sizable contribution to improving the user experience by dealing with utterance collapse in natural conversation. An introduction video of our system is also available at https://youtu.be/TG1pJJo0Iqo.