Schema-Driven Actionable Insight Generation and Smart Recommendation

TL;DR

This paper presents a schema-driven approach for generating and ranking actionable insights from data in natural language generation, aiming to improve relevance and user engagement through feedback adaptation.

Contribution

It introduces a novel schema-based method for insight generation and a ranking technique that adapts to user feedback, addressing the challenges of multidimensionality and subjectivity.

Findings

Preliminary qualitative results demonstrate effective insight generation.

The ranking technique aligns insights with user interests based on feedback.

The approach adapts to user feedback to improve relevance.

Abstract

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.