Schema-Driven Actionable Insight Generation and Smart Recommendation
Allmin Susaiyah Aki Härmä Milan Petković
In natural language generation (NLG), insight mining is seen as a
data-to-text task, where data is mined for interesting patterns and verbalised
into 'insight' statements. An 'over-generate and rank' paradigm is intuitively
used to generate such insights. The multidimensionality and subjectivity of
this process make it challenging. This paper introduces a schema-driven method
to generate actionable insights from data to drive growth and change. It also
introduces a technique to rank the insights to align with user interests based
on their feedback. We show preliminary qualitative results of the insights
generated using our technique and demonstrate its ability to adapt to feedback.