Schema-Driven Actionable Insight Generation and Smart Recommendation
This work addresses the problem of producing user-aligned insights for data analysis, but it appears incremental as it builds on existing 'over-generate and rank' paradigms without major breakthroughs.
The paper tackles the challenge of generating and ranking actionable insights from data in natural language generation by introducing a schema-driven method and a feedback-based ranking technique, showing preliminary qualitative results and adaptability to user feedback.
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.