Aspect-Aware Decomposition for Opinion Summarization

• URL: http://arxiv.org/abs/2501.17191v2
• Date: Tue, 18 Feb 2025 06:30:09 GMT
• Title: Aspect-Aware Decomposition for Opinion Summarization
• Authors: Miao Li, Jey Han Lau, Eduard Hovy, Mirella Lapata,
• Abstract summary: We propose a modular approach guided by review aspects which separates the tasks of aspect identification, opinion consolidation, and meta-review synthesis. We conduct experiments across datasets representing scientific research, business, and product domains. Results show that our method generates more grounded summaries compared to strong baseline models.
• Score: 82.38097397662436
• License:
• Abstract: Opinion summarization plays a key role in deriving meaningful insights from large-scale online reviews. To make this process more explainable and grounded, we propose a modular approach guided by review aspects which separates the tasks of aspect identification, opinion consolidation, and meta-review synthesis, enabling greater transparency and ease of inspection. We conduct extensive experiments across datasets representing scientific research, business, and product domains. Results show that our method generates more grounded summaries compared to strong baseline models, as verified through automated and human evaluations. Additionally, our modular approach, which incorporates reasoning based on review aspects, produces more informative intermediate outputs than knowledge-agnostic decomposed prompting. These intermediate outputs can also effectively support humans in summarizing opinions from large volumes of reviews.

Related papers

• Aspect-Guided Multi-Level Perturbation Analysis of Large Language Models in Automated Peer Review [36.05498398665352]
  We propose an aspect-guided, multi-level perturbation framework to evaluate the robustness of Large Language Models (LLMs) in automated peer review. Our framework explores perturbations in three key components of the peer review process-papers, reviews, and rebuttals-across several quality aspects.
  arXiv  Detail & Related papers  (2025-02-18T03:50:06Z)
• Generative Adversarial Reviews: When LLMs Become the Critic [1.2430809884830318]
  We introduce Generative Agent Reviewers (GAR), leveraging LLM-empowered agents to simulate faithful peer reviewers. Central to this approach is a graph-based representation of manuscripts, condensing content and logically organizing information. Our experiments demonstrate that GAR performs comparably to human reviewers in providing detailed feedback and predicting paper outcomes.
  arXiv  Detail & Related papers  (2024-12-09T06:58:17Z)
• Large-Scale and Multi-Perspective Opinion Summarization with Diverse Review Subsets [23.515892409202344]
  SUBSUMM is a supervised summarization framework for large-scale multi-perspective opinion summarization. It generates pros, cons, and verdict summaries from hundreds of input reviews.
  arXiv  Detail & Related papers  (2023-10-20T08:08:13Z)
• Human-in-the-loop Abstractive Dialogue Summarization [61.4108097664697]
  We propose to incorporate different levels of human feedback into the training process. This will enable us to guide the models to capture the behaviors humans care about for summaries.
  arXiv  Detail & Related papers  (2022-12-19T19:11:27Z)
• Learning Opinion Summarizers by Selecting Informative Reviews [81.47506952645564]
  We collect a large dataset of summaries paired with user reviews for over 31,000 products, enabling supervised training. The content of many reviews is not reflected in the human-written summaries, and, thus, the summarizer trained on random review subsets hallucinates. We formulate the task as jointly learning to select informative subsets of reviews and summarizing the opinions expressed in these subsets.
  arXiv  Detail & Related papers  (2021-09-09T15:01:43Z)
• Aspect-Controllable Opinion Summarization [58.5308638148329]
  We propose an approach that allows the generation of customized summaries based on aspect queries. Using a review corpus, we create a synthetic training dataset of (review, summary) pairs enriched with aspect controllers. We fine-tune a pretrained model using our synthetic dataset and generate aspect-specific summaries by modifying the aspect controllers.
  arXiv  Detail & Related papers  (2021-09-07T16:09:17Z)
• Unsupervised Opinion Summarization with Content Planning [58.5308638148329]
  We show that explicitly incorporating content planning in a summarization model yields output of higher quality. We also create synthetic datasets which are more natural, resembling real world document-summary pairs. Our approach outperforms competitive models in generating informative, coherent, and fluent summaries.
  arXiv  Detail & Related papers  (2020-12-14T18:41:58Z)
• OpinionDigest: A Simple Framework for Opinion Summarization [22.596995566588422]
  The framework 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. The selected opinions are used as input to the trained Transformer model, which verbalizes them into an opinion summary. OpinionDigest can also generate customized summaries, tailored to specific user needs, by filtering the selected opinions according to their aspect and/or sentiment.
  arXiv  Detail & Related papers  (2020-05-05T01:22:29Z)
• Few-Shot Learning for Opinion Summarization [117.70510762845338]
  Opinion summarization is the automatic creation of text reflecting subjective information expressed in multiple documents. In this work, we show that even a handful of summaries is sufficient to bootstrap generation of the summary text. Our approach substantially outperforms previous extractive and abstractive methods in automatic and human evaluation.
  arXiv  Detail & Related papers  (2020-04-30T15:37:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.