Attributable and Scalable Opinion Summarization
Tom Hosking Hao Tang Mirella Lapata
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. We also demonstrate that our appraoch enables a degree of control,
generating aspect-specific summaries by restricting the model to parts of the
encoding space that correspond to desired aspects (e.g., location or food).
Automatic and human evaluation on two datasets from different domains
demonstrates that our method generates summaries that are more informative than
prior work and better grounded in the input reviews.