Integrating Latent Aspect Modelling with Gated Attention LSTM for Suggestion Summarization
DOI:
https://doi.org/10.48047/y8djdg19Keywords:
.Abstract
User-generated reviews and feedback often contain actionable suggestions that can
drive product improvements. In this paper, we present an aspect-based suggestion
summarization framework that leverages a Gated Attention LSTM to learn latent suggestion
aspects without the need for predefined categories. Our model computes pairwise aspect
Matching to cluster suggestions and uses an extractive summarization strategy that balances
semantic relevance with aspect diversity.
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