natural-language-processing-2

Opinion Summarization

3260 papers • 126 benchmarks • 313 datasets

The task of generating a summary of user opinions from reviews (and question-answers, etc)

(Image credit: Papersgraph)

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Most implemented papers

Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised

Mirella Lapata, S. Angelidis•Tue Jul 31 2018

An opinion summarization dataset is introduced that includes a training set of product reviews from six diverse domains and human-annotated development and test sets with gold standard aspect annotations, salience labels, and opinion summaries.

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Extractive Opinion Summarization in Quantized Transformer Spaces

Mirella Lapata, Yoshihiko Suhara, S. Angelidis, Reinald Kim Amplayo, Xiaolan Wang•Mon Dec 07 2020

The Quantized Transformer is inspired by Vector- Quantized Variational Autoencoders and uses a clustering interpretation of the quantized space and a novel extraction algorithm to discover popular opinions among hundreds of reviews, a significant step towards opinion summarization of practical scope.

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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.

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Informative and Controllable Opinion Summarization

Mirella Lapata, Reinald Kim Amplayo•Wed Sep 04 2019

This paper proposes a summarization framework that eliminates the need to rely only on pre-selected content and waste possibly useful information, especially when customizing summaries, and enables the use of all input reviews by first condensing them into multiple dense vectors which serve as input to an abstractive model.

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Target-oriented Opinion Words Extraction with Target-fused Neural Sequence Labeling

Shujian Huang, Jiajun Chen, Xinyu Dai, Zhifang Fan, Zhen Wu•Fri May 31 2019

This paper proposes a novel sequence labeling subtask for ABSA named TOWE (Target-oriented Opinion Words Extraction), which aims at extracting the corresponding opinion words for a given opinion target through a target-fused sequence labeling neural network model.

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OpinionDigest: A Simple Framework for Opinion Summarization

Yoshihiko Suhara, W. Tan, S. Angelidis, Xiaolan Wang•Mon May 04 2020

OpinionDigest, an abstractive opinion summarization framework, which uses an Aspect-based Sentiment Analysis model to extract opinion phrases from reviews, and trains a Transformer model to reconstruct the original reviews from these extractions.

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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.

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Few-Shot Learning for Opinion Summarization

Mirella Lapata, Ivan Titov, Arthur Bravzinskas•Wed Apr 29 2020

This work shows that even a handful of summaries is sufficient to bootstrap generation of the summary text with all expected properties, such as writing style, informativeness, fluency, and sentiment preservation.

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Weakly-Supervised Opinion Summarization by Leveraging External Information

Snigdha Chaturvedi, Chao Zhao•Thu Nov 21 2019

This work proposes AspMem, a generative method that contains an array of memory cells to store aspect-related knowledge that can help obtain a better opinion representation and infer the aspect information more precisely.

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Read what you need: Controllable Aspect-based Opinion Summarization of Tourist Reviews

Pawan Goyal, Niloy Ganguly, Rajdeep Mukherjee, Sourangshu Bhattacharya, Hari Chandana Peruri, Uppada Vishnu•Sun Jun 07 2020

This work argues the need and proposes a solution for generating personalized aspect-based opinion summaries from large collections of online tourist reviews, and takes an unsupervised approach to extract coherent aspects from tourist reviews posted on TripAdvisor.

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