| Challenge: | Recent work on opinion summarization produces general summaries based on reviews and popularity of opinions expressed in them. |
| Approach: | They propose an approach that generates customized opinion summaries based on aspect queries. |
| Outcome: | The proposed model outperforms the current state of the art and generates personalized summaries by controlling the number of aspects discussed in them. |
Similar Papers
Inducing Document Structure for Aspect-based Summarization (P19-1)
Copied to clipboard
| Challenge: | Abstractive summarization systems treat documents as unstructured and generate a single generic summary per document. |
| Approach: | They propose to incorporate document structure into automatic summarization systems . they induce latent document structure and abstractive summarizing objective . |
| Outcome: | The proposed model improves on topic-agnostic baselines and can produce abstractive and extractive aspect-based summaries. |
Self-Supervised and Controlled Multi-Document Opinion Summarization (2021.eacl-main)
Copied to clipboard
| Challenge: | Existing unsupervised methods for summarizing reviews are based on bootstrapping and require a combination of loss functions or hierarchical latent variables to ensure that the generated summaries remain on-topic. |
| Approach: | They propose a self-supervised setup that considers an individual document as a target summary for a set of similar documents. |
| Outcome: | The proposed setup makes training simpler than previous approaches by relying only on standard log-likelihood loss and mainstream models. |
Unsupervised Aspect-Based Multi-Document Abstractive Summarization (D19-54)
Copied to clipboard
| Challenge: | Existing methods for opinion summarization are expensive and do not deal with contradictory statements. |
| Approach: | They propose an unsupervised abstractive summarization neural system that generates short summaries of reviews in a vector space. |
| Outcome: | The proposed system can generate short summaries of user-generated reviews in a short paragraph, while nobody reads all reviews. |
Summarizing Text on Any Aspects: A Knowledge-Informed Weakly-Supervised Approach (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing studies on aspect-based abstractive summarization assume a small set of aspects and do not consider other diverse aspects. |
| Approach: | They propose a weak supervision construction method and an aspect modeling scheme to solve this problem. |
| Outcome: | The proposed method significantly expands the application of the task in practice. |
OpinionDigest: A Simple Framework for Opinion Summarization (2020.acl-main)
Copied to clipboard
| Challenge: | Abstractive opinion summarization framework outperforms competitors' summarizing frameworks . extractive approaches produce well-formed text, but selecting the most popular opinions is challenging . |
| Approach: | They propose an abstractive opinion summarization framework that trains a Transformer model to reconstruct reviews from extracted opinions. |
| Outcome: | The proposed framework outperforms baselines on Yelp and shows promising customization capabilities. |
WikiAsp: A Dataset for Multi-domain Aspect-based Summarization (2021.tacl-1)
Copied to clipboard
| Challenge: | Existing aspects-based summarization models are domain-specific due to large differences in the type of aspects for different domains. |
| Approach: | They propose a large-scale dataset for multi-domain aspect-based summarization using Wikipedia articles from 20 different domains. |
| Outcome: | The proposed model is based on Wikipedia articles from 20 different domains and uses the section titles and boundaries of each article as a proxy for aspect annotation. |
Opinion Summarization by Weak-Supervision from Mix-structured Data (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for opinion summarization of multiple reviews lack reference summaries . OAs and ISs are often mismatched between review input and summary . |
| Approach: | They propose a method to generate mixed-structured synthetic training data for opinion summarization. |
| Outcome: | The proposed method outperforms existing methods on Yelp, Amazon and RottenTomatos datasets. |
Informative and Controllable Opinion Summarization (2021.eacl-main)
Copied to clipboard
| Challenge: | Existing methods for opinion summarization use a two-stage extractive and abstractive approach to generate summaries for reviews of a specific target. |
| Approach: | They propose a framework for opinion summarization that condenses all input reviews into multiple dense vectors which serve as input to an abstractive model. |
| Outcome: | The proposed framework produces more informative summaries and allows to take user preferences into account using a zero-shot customization technique. |
Few-Shot Learning for Opinion Summarization (2020.emnlp-main)
Copied to clipboard
| Challenge: | a recent study shows that abstractive summarization models fail to capture their essential properties due to the high cost of summary production. |
| Approach: | They propose a few-shot framework for abstractive opinion summarization that bootstraps the output of an unsupervised model. |
| Outcome: | The proposed framework outperforms extractive and abstractive methods on Amazon and Yelp datasets. |
Unsupervised Opinion Summarization as Copycat-Review Generation (2020.acl-main)
Copied to clipboard
| Challenge: | Recent work on opinion summarization has focused on extracting fragments from reviews, but we use novel sentences to generate abstractive summaries. |
| Approach: | They propose an abstractive summarizer which does not use summaries in training and is trained end-to-end on a large collection of reviews. |
| Outcome: | The proposed model produces fluent and coherent summaries reflecting consensus opinions on Amazon and Yelp reviews. |