Aspect-Based Opinion Summarization with Argumentation Schemes
- URL: http://arxiv.org/abs/2506.09917v2
- Date: Thu, 12 Jun 2025 14:45:40 GMT
- Title: Aspect-Based Opinion Summarization with Argumentation Schemes
- Authors: Wendi Zhou, Ameer Saadat-Yazdi, Nadin Kokciyan,
- Abstract summary: It is impractical for customers to go over the vast number of reviews and manually conclude the prominent opinions.<n>Previous approaches, either extractive or abstractive, face challenges in automatically producing grounded aspect-centric summaries.<n>We propose a novel summarization system that not only captures predominant opinions from an aspect perspective with supporting evidence, but also adapts to varying domains without relying on a pre-defined set of aspects.
- Score: 0.13654846342364307
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Reviews are valuable resources for customers making purchase decisions in online shopping. However, it is impractical for customers to go over the vast number of reviews and manually conclude the prominent opinions, which prompts the need for automated opinion summarization systems. Previous approaches, either extractive or abstractive, face challenges in automatically producing grounded aspect-centric summaries. In this paper, we propose a novel summarization system that not only captures predominant opinions from an aspect perspective with supporting evidence, but also adapts to varying domains without relying on a pre-defined set of aspects. Our proposed framework, ASESUM, summarizes viewpoints relevant to the critical aspects of a product by extracting aspect-centric arguments and measuring their salience and validity. We conduct experiments on a real-world dataset to demonstrate the superiority of our approach in capturing diverse perspectives of the original reviews compared to new and existing methods.
Related papers
- Identifying Aspects in Peer Reviews [61.374437855024844]
We develop a data-driven schema for deriving aspects from a corpus of peer reviews.<n>We introduce a dataset of peer reviews augmented with aspects and show how it can be used for community-level review analysis.
arXiv Detail & Related papers (2025-04-09T14:14:42Z) - Decomposed Opinion Summarization with Verified Aspect-Aware Modules [82.38097397662436]
We propose a domain-agnostic modular approach guided by review aspects.<n>We conduct experiments across datasets representing scientific research, business, and product domains.
arXiv Detail & Related papers (2025-01-27T09:29:55Z) - GLIMPSE: Pragmatically Informative Multi-Document Summarization for Scholarly Reviews [25.291384842659397]
We introduce sys, a summarization method designed to offer a concise yet comprehensive overview of scholarly reviews.
Unlike traditional consensus-based methods, sys extracts both common and unique opinions from the reviews.
arXiv Detail & Related papers (2024-06-11T15:27:01Z) - Towards Personalized Review Summarization by Modeling Historical Reviews
from Customer and Product Separately [59.61932899841944]
Review summarization is a non-trivial task that aims to summarize the main idea of the product review in the E-commerce website.
We propose the Heterogeneous Historical Review aware Review Summarization Model (HHRRS)
We employ a multi-task framework that conducts the review sentiment classification and summarization jointly.
arXiv Detail & Related papers (2023-01-27T12:32:55Z) - Latent Aspect Detection from Online Unsolicited Customer Reviews [3.622430080512776]
Aspect detection helps product owners and service providers to identify shortcomings and prioritize customers' needs.
Existing methods focus on detecting the surface form of an aspect by training supervised learning methods that fall short when aspects are latent in reviews.
We propose an unsupervised method to extract latent occurrences of aspects.
arXiv Detail & Related papers (2022-04-14T13:46:25Z) - 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) - Summarizing Text on Any Aspects: A Knowledge-Informed Weakly-Supervised
Approach [89.56158561087209]
We study summarizing on arbitrary aspects relevant to the document.
Due to the lack of supervision data, we develop a new weak supervision construction method and an aspect modeling scheme.
Experiments show our approach achieves performance boosts on summarizing both real and synthetic documents.
arXiv Detail & Related papers (2020-10-14T03:20:46Z) - Read what you need: Controllable Aspect-based Opinion Summarization of
Tourist Reviews [23.7107052882747]
We argue the need and propose a solution for generating personalized aspect-based opinion summaries from online tourist reviews.
We let our readers decide and control several attributes of the summary such as the length and specific aspects of interest.
Specifically, we take an unsupervised approach to extract coherent aspects from tourist reviews posted on TripAdvisor.
arXiv Detail & Related papers (2020-06-08T15:03:38Z) - Topic Detection and Summarization of User Reviews [6.779855791259679]
We propose an effective new summarization method by analyzing both reviews and summaries.
A new dataset comprising product reviews and summaries about 1028 products are collected from Amazon and CNET.
arXiv Detail & Related papers (2020-05-30T02:19:08Z)
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.