Attributable and Scalable Opinion Summarization

Understand

We propose a method for unsupervised opinion summarization that encodes sentences from customer reviews into a hierarchical discrete latent space, then identifies common opinions based on the frequency of their encodings.

• We are able to generate both abstractive summaries by decoding these frequent encodings, and extractive summaries by selecting the sentences assigned to the same frequent encodings.
• Our method is attributable, because the model identifies sentences used to generate the summary as part of the summarization process.
• It scales easily to many hundreds of input reviews, because aggregation is performed in the latent space rather than over long sequences of tokens.