Unsupervised Extractive Opinion Summarization Using Sparse Coding
This addresses the problem of summarizing user reviews for applications like e-commerce, though it is incremental as it builds on existing unsupervised methods.
The paper tackles unsupervised extractive opinion summarization by introducing Semantic Autoencoder (SemAE), which uses dictionary learning to capture semantic concepts and identify representative sentences from reviews, achieving strong performance on SPACE and AMAZON datasets.
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.