Unsupervised Neural Single-Document Summarization of Reviews via Learning Latent Discourse Structure and its Ranking (P19-1)
Copied to clipboard
| Challenge: | Currently, unsupervised summarization is widely used for product reviews on E-commerce websites. |
| Approach: | They propose an unsupervised model that learns the latent discourse tree without an external parser and generates a concise summary. |
| Outcome: | The proposed model outperforms other unsupervised approaches for relatively long reviews and is competitive with or better than supervised models. |
Similar Papers
A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents (N18-2)
Copied to clipboard
Arman Cohan, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Seokhwan Kim, Walter Chang, Nazli Goharian
| Challenge: | Existing abstractive summarization models focus on summarizing sentences and short documents. |
| Approach: | They propose a hierarchical encoder that models the discourse structure of a document, and an attentive discourse-aware decoder to generate the summary. |
| Outcome: | The proposed model significantly outperforms state-of-the-art models on two large-scale datasets of scientific papers. |
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. |
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. |
Unsupervised Abstractive Opinion Summarization by Generating Sentences with Tree-Structured Topic Guidance (2021.tacl-1)
Copied to clipboard
| Challenge: | Abstractive summarization is a novel method for opinionated texts . it uses a recursive Gaussian mixture to generate topic sentences . |
| Approach: | They propose an unsupervised abstractive summarization method for opinionated texts . they alternate the unimodal Gaussian prior with a recursive Gausssian mixture . |
| Outcome: | The proposed method generates topic sentences with tree-structured topic guidance, which are more informative and cover more input contents than the current model. |
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. |
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. |
Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization (D18-1)
Copied to clipboard
| Challenge: | Existing approaches to summarize documents are not extractive and require an abstractive approach. |
| Approach: | They propose a novel abstractive model which is conditioned on the article’s topics and based entirely on convolutional neural networks. |
| Outcome: | The proposed model outperforms an oracle extractive system and state-of-the-art abstractive approaches when evaluated automatically and by humans. |
Attributable and Scalable Opinion Summarization (2023.acl-long)
Copied to clipboard
| Challenge: | Existing methods for opinion summarization encode sentences from customer reviews into a hierarchical discrete latent space. |
| Approach: | They propose a method that encodes customer reviews into a hierarchical discrete latent space and then identifies common opinions based on their frequency. |
| Outcome: | The proposed method generates summaries that are more informative than previous work and more grounded in the input reviews. |
Learning From the Source Document: Unsupervised Abstractive Summarization (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing methods for abstractive summarization are under supervised training, but obtaining high-quality and large-scale datasets for supervised learning is laboriously difficult. |
| Approach: | They propose an unsupervised method that leverages contrastive learning to generate summaries by rewriting and paraphrasing the source documents to generate good summary. |
| Outcome: | The proposed method outperforms baseline methods on extensive experiments on source documents and fake documents. |
Unsupervised Opinion Summarization with Noising and Denoising (2020.acl-main)
Copied to clipboard
| Challenge: | Existing methods for abstractive summarization are limited and cannot be easily sourced. |
| Approach: | They propose a supervised learning model which learns to denoise the input and generate original reviews. |
| Outcome: | The proposed model improves on the baselines of abstractive and extractive models on a large dataset with only a few reviews and no ground truth summaries. |