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natural-language-processing-11

Unsupervised Opinion Summarization

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

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Yelp

Amazon

SPACE (Opinion Summarization)

Libraries

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Yelp

AmaSum

SPACE (Opinion Summarization)

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Most implemented papers

Unsupervised Opinion Summarization as Copycat-Review Generation

Mirella Lapata, Arthur Brazinskas, Ivan Titov•Tue Nov 05 2019

A generative model for a review collection is defined which capitalizes on the intuition that when generating a new review given a set of other reviews of a product, the authors should be able to control the “amount of novelty” going into the new review or, equivalently, vary the extent to which it deviates from the input.

139

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Unsupervised Opinion Summarization with Content Planning

Mirella Lapata, S. Angelidis, Reinald Kim Amplayo•Sun Dec 13 2020

This work shows that explicitly incorporating content planning in a summarization model not only yields output of higher quality, but also allows the creation of synthetic datasets which are more natural, resembling real world document-summary pairs.

51 0

Paper Graph

Unsupervised Opinion Summarization with Noising and Denoising

Mirella Lapata, Reinald Kim Amplayo•Mon Apr 20 2020

This paper enables the use of supervised learning for the setting where there are only documents available without ground truth summaries, and introduces several linguistically motivated noise generation functions and a summarization model which learns to denoise the input and generate the original review.

72 0

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Convex Aggregation for Opinion Summarization

Yoshihiko Suhara, W. Tan, Hayate Iso, S. Angelidis, Xiaolan Wang•Fri Apr 02 2021

A framework Coop is developed, which searches input combinations for the latent vector aggregation using input-output word overlap and successfully alleviates the summary vector degeneration issue and establishes new state-of-the-art performance on two opinion summarization benchmarks.

37 0

Paper Graph

Attributable and Scalable Opinion Summarization

Mirella Lapata, Tom Hosking, Hao Tang•Thu May 18 2023

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, which generates summaries that are more informative than prior work and better grounded in the input reviews.

10 0

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Unsupervised Opinion Summarization | State-of-the-Art