Papers with opinion summarization
Unsupervised Aspect-Based Multi-Document Abstractive Summarization (D19-54)
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| 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. |
Extractive Opinion Summarization in Quantized Transformer Spaces (2021.tacl-1)
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| Challenge: | Existing work on opinion summarization focuses on aggregating opinions among reviews . et al., 2018; see etal., 2019; liu eto, 2019) demonstrate the potential of opinion summaries. |
| Approach: | They propose an unsupervised system for extractive opinion summarization based on vector-quantized variables and an extraction algorithm. |
| Outcome: | The proposed method is validated by human studies showing that judges prefer it over baselines. |
Self-Supervised Multimodal Opinion Summarization (2021.acl-long)
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| Challenge: | Existing methods for opinion summarization use text data, but non-text data are less abundant. |
| Approach: | They propose a self-supervised opinion summarization framework that uses non-text data to generate a summary from multiple reviews. |
| Outcome: | The proposed framework is superior to existing methods on Yelp and Amazon datasets. |
Efficient Few-Shot Fine-Tuning for Opinion Summarization (2022.findings-naacl)
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| Challenge: | Abstractive summarization models are typically pre-trained on large amounts of generic texts . large annotated datasets of reviews paired with reference summaries are not available . |
| Approach: | They propose a few-shot method which uses adapters to store in-domain knowledge . they pre-train adapters on unannotated customer reviews and fine-tune them on annotated datasets . |
| Outcome: | The proposed method can store in-domain knowledge and improves on large annotated reviews . it improves coherence and redundancies on the Amazon and Yelp datasets . |
Product Description and QA Assisted Self-Supervised Opinion Summarization (2024.findings-naacl)
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Tejpalsingh Siledar, Rupasai Rangaraju, Sankara Muddu, Suman Banerjee, Amey Patil, Sudhanshu Singh, Muthusamy Chelliah, Nikesh Garera, Swaprava Nath, Pushpak Bhattacharyya
| Challenge: | Existing methods to generate opinion summarization without supervised training data are limited due to the lack of additional sources. |
| Approach: | They propose a synthetic dataset creation strategy that leverages reviews and additional sources to generate a pseudo-summary. |
| Outcome: | The proposed approach achieves 14.5% improvement in ROUGE-1 F1 over existing models. |
Recursive Neural Structural Correspondence Network for Cross-domain Aspect and Opinion Co-Extraction (P18-1)
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| Challenge: | supervised learning methods have been used for fine-grained opinion analysis but lack of labeled data hinders learning . authors develop a recursive neural network that could reduce domain shift in word level . a recent paper shows that unsupervised methods fail to adapt well across domains . |
| Approach: | They propose a supervised neural network that reduces domain shift effectively in word level . they treat these relations as invariant "pivot information" across domains to build structural correspondences . |
| Outcome: | The proposed model reduces domain shift effectively in word level through syntactic relations . it can be used to predict the relation between two adjacent words in the dependency tree . |
Summarizing Opinions: Aspect Extraction Meets Sentiment Prediction and They Are Both Weakly Supervised (D18-1)
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| Challenge: | Existing methods for opinion summarization are knowledge-lean and require light supervision. |
| Approach: | They propose a neural framework for opinion summarization from online product reviews which is knowledge-lean and only requires light supervision. |
| Outcome: | The proposed framework improves over baselines and shows that opinion summaries are preferred by human judges according to multiple criteria. |
Aspect-Controllable Opinion Summarization (2021.emnlp-main)
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| 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. |
Synchronous Double-channel Recurrent Network for Aspect-Opinion Pair Extraction (2020.acl-main)
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| Challenge: | Existing studies focus on aspect-opinion relation detection, but neglect to recognize the relations between aspects and opinion expressions. |
| Approach: | They propose a Synchronous Double-channel Recurrent Network to deal with AOPE task . they propose an opinion entity extraction unit, a relation detection unit, and a synchronization unit . |
| Outcome: | The proposed system achieves state-of-the-art in opinion entity extraction . it is based on three datasets based upon SemEval 2014 and 2015 benchmarks . |
Prompted Opinion Summarization with GPT-3.5 (2023.findings-acl)
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| Challenge: | Recent years have seen several shifts in summarization research, including extractive models. |
| Approach: | They propose a pipeline method for applying GPT-3.5 to summarize user reviews . they propose three new metrics targeting faithfulness, factuality, and genericity . |
| Outcome: | The proposed methods perform well in opinion summarization, the authors show . they also show that standard evaluation metrics do not reflect this performance . |
Synthesize, if you do not have: Effective Synthetic Dataset Creation Strategies for Self-Supervised Opinion Summarization in E-commerce (2023.findings-emnlp)
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Tejpalsingh Siledar, Suman Banerjee, Amey Patil, Sudhanshu Singh, Muthusamy Chelliah, Nikesh Garera, Pushpak Bhattacharyya
| Challenge: | Existing approaches to generate general and aspect-specific opinion summarization are limited due to their reliance on human-specified aspects and seed words. |
| Approach: | They propose synthetic dataset creation approaches for general and aspect-specific opinion summarization . general opinion summaries struggle to generate faithful to the input reviews, they say . aspect- specific opinion summarisation models are limited due to reliance on human-specified aspects . |
| Outcome: | The proposed approach outperforms existing models on three e-commerce test sets on general and aspect-specific opinion summarization. |
ReflectSumm: A Benchmark for Course Reflection Summarization (2024.lrec-main)
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| Challenge: | Existing research has focused on standard summarization benchmarks within domains like news, scientific articles, and opinions. |
| Approach: | They propose a summarization dataset specifically designed for summarizing students’ reflective writing. |
| Outcome: | The proposed summarization dataset can be used in opinion summarizing scenarios and in educational domains. |