Unsupervised Opinion Summarization
5 papers with code • 3 benchmarks • 3 datasets
Most implemented papers
Unsupervised Opinion Summarization as Copycat-Review Generation
At test time, when generating summaries, we force the novelty to be minimal, and produce a text reflecting consensus opinions.
Unsupervised Opinion Summarization with Noising and Denoising
We create a synthetic dataset from a corpus of user reviews by sampling a review, pretending it is a summary, and generating noisy versions thereof which we treat as pseudo-review input.
Unsupervised Opinion Summarization with Content Planning
The recent success of deep learning techniques for abstractive summarization is predicated on the availability of large-scale datasets.
Convex Aggregation for Opinion Summarization
We found that text autoencoders tend to generate overly generic summaries from simply averaged latent vectors due to an unexpected $L_2$-norm shrinkage in the aggregated latent vectors, which we refer to as summary vector degeneration.
Attributable and Scalable Opinion Summarization
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.