  We propose InsightNet, a novel approach for the automated extraction of
structured insights from customer reviews. Our end-to-end machine learning
framework is designed to overcome the limitations of current solutions,
including the absence of structure for identified topics, non-standard aspect
names, and lack of abundant training data. The proposed solution builds a
semi-supervised multi-level taxonomy from raw reviews, a semantic similarity
heuristic approach to generate labelled data and employs a multi-task insight
extraction architecture by fine-tuning an LLM. InsightNet identifies granular
actionable topics with customer sentiments and verbatim for each topic.
Evaluations on real-world customer review data show that InsightNet performs
better than existing solutions in terms of structure, hierarchy and
completeness. We empirically demonstrate that InsightNet outperforms the
current state-of-the-art methods in multi-label topic classification, achieving
an F1 score of 0.85, which is an improvement of 11% F1-score over the previous
best results. Additionally, InsightNet generalises well for unseen aspects and
suggests new topics to be added to the taxonomy.
