  Opinion summarization is the task of automatically generating summaries that
encapsulate information from multiple user reviews. We present Semantic
Autoencoder (SemAE) to perform extractive opinion summarization in an
unsupervised manner. SemAE uses dictionary learning to implicitly capture
semantic information from the review and learns a latent representation of each
sentence over semantic units. A semantic unit is supposed to capture an
abstract semantic concept. Our extractive summarization algorithm leverages the
representations to identify representative opinions among hundreds of reviews.
SemAE is also able to perform controllable summarization to generate
aspect-specific summaries. We report strong performance on SPACE and AMAZON
datasets, and perform experiments to investigate the functioning of our model.
Our code is publicly available at https://github.com/brcsomnath/SemAE.
