Inducing Argument Facets for Faithful Opinion Summarization (2025)

Abstract: Faithful opinion summarization task refers to generating a summary for a set of documents that covers the majority and minority opinions in documents. Inspired by the cognitive science that argument facet is the focus of an opinion, we propose the facets-guided opinion summarization method (FacSum). By inducing the facets, we partition the documents into multiple facet-specific sets. Then key phrases are extracted as the representatives of each set and the number of facets is used for constraining the length of summary, both of which are used to guide large language models (LLMs) to cover different argument facets of opinions while keeping the summary concise. We perform experiments on two representative datasets and the results show that our method outperforms the state-of-the-art (SOTA) methods and multiple LLMs. The ablation studies indicate that the introduced facets contribute to improving model performance by enabling the coverage of minority opinions while preserving the majority ones. The results based on different LLMs demonstrate that our method can improve the performance of LLMs with varying model sizes. We apply FacSum to the summarization of professional paper reviews, and the results confirm its effectiveness in specialty domains as well.

## introduction for a topic, there are often many different opinions. some opinions reflect the common concerns of social groups and are generally recognized as the majority opinions. in contrast, minority opinions, although expressed less frequently, are equally important since they reveal potential problems, risks, or neglected demands @xcite . our faithful opinion summarization task @xcite @xcite aims to generate concise summaries that accurately capture both * corresponding author. the majority and minority opinions in a given opinion document set. this task is crucial in scenarios such as the public policy making to help decisionmakers quickly and comprehensively understand different opinions so as to prevent decision bias. recently, llms have become the main choice for the above task @xcite @xcite . some methods focus on constructing high-quality prompts for guiding llms @xcite . they use a self-evaluation framework that evaluates the generated summaries by llm and construct feedback to calibrate the prompts. while these methods can guide llms to generate summaries containing different opinions, they come with high computational overhead. other methods decompose the summarization task into multiple simpler subtasks using a chain-of-thought (cot) approach @xcite , leveraging the strengths of llms on simpler tasks @xcite . for example, the tcg method clusters the documents, generates a sub-summary for each cluster, and then merges all sub-summaries into a complete summary @xcite . these methods improve the semantic coverage of summaries to source documents. however, due to the lack of explicit guidance for minority opinions, some marginal opinions are easily overlooked during the clustering and generation processes. to generate summaries that cover both majority and minority opinions while maintaining concise, we propose the facet-guided opinion summarization method (facsum). here, a 'facet' refers to a specific latent statement dimensions of an opinion @xcite , such as for the topic "should schools offer cash bonuses for good test scores ", the facets including "doubts about practicality and feasibility", "concerns about bribery and ethics". facsum explicitly highlights the latent facets within the documents to construct prompts that can guide the attention of llms. the facetsensitive prompt includes two kinds of guidance, where the semantics guidance considers the facet coverage in documents for guiding llms to cover both the majority and minority opinions with all facets, and the format guidance by the number of facets is used to constrain and organize opinions for reducing redundancy. specifically, we first use an llm to induce the topic and facets within the document set. then, based on the identified facet of each document, the source documents are grouped as multiple facetspecific sets. next, key phrases are extracted to represent each subset and are subsequently merged to form a comprehensive set of facet representations. these phrases form the semantics guidance. compared to directly extracting key phrases from the document sets, our method can capture the phrases from majority opinion while retaining those from minority opinions. the number of facets is used for constraining the maximum length of summary since the more facets involved in documents, the more content in summary. to guide llms in organizing summaries in a logical manner, we design the narration template, which is used together with the above guidance. we perform experiments on two social datasets and a scientific dataset. the results demonstrate that our method outperforms the sota methods and several llms. ## related work for the opinion summarization task, since the annotated data is difficult to obtain, the mainstream methods have focused on unsupervised settings and can be categorized into two types. the first type adopts the domain knowledge to pre-trains a summarization model, while the second type explores how to directly leverage the knowledge encoded in llms. ## problem and framework given a document set @xmath0, d 2 , • • • , d |d| }, the faithful opinion summarization task aims to generate a summary s that captures both majority and minority opinions within d. for this task, we propose the facet-guided summarization method (fac-sum) that induces the facets in d for guiding llms to focus on these opinions. we use 'facet' to denote the fine-grained argumentative dimensions of an opinion (e.g., vaccination safety for the elderly for the topic 'vaccination') @xcite , as opposed to broader aspect (e.g., vaccination risks) used in prior works @xcite , allowing for a finer delineation of opinion diversity. as shown in fig. 1 , our method contains two parts: to induce the facets from documents and to construct the facet related guidance for summarization. we first induce the facets contained in documents by llms, based on which the documents d are grouped into multiple facet-specific sets. as a result, each document set correspond to a facet. then key phrases are extracted from each set of documents as the representative of the corresponding facet. we merge all the key phrases from each facetspecific set together for constructing the prompt that contains the semantics and format guidance. the semantic guidance encourages the llm to attend to all facets of opinion, while the format guidance helps organize the multiple opinions in a structured manner to reduce redundancy. ## adapting facsum to specialty domains we explore whether facsum can be applied to summarization tasks in specialized domains, such as the summarization task on paper reviews. paper reviews typically employ formal, academic language characterized by logical reasoning and analytical rigor, which differs from the social documents in eo and co datasets. in this setting, our task aims to summarize multiple paper reviews into a metareview that contains the consensuses and controversies @xcite . we perform experiments on the orsum dataset @xcite , which is constructed by collecting open-sourced papers and meta-reviews from open-review foot_5 . the dataset contains 15062 meta-reviews and 57536 reviews from 47 conference venues. ## conclusion in this paper, to generate faithful opinion summaries, we propose the facet-guided summarization method, facsum. we induce facets to construct fine-grained prompts that contain both semantic guidance and format guidance. the semantic guidance is constructed by extracting facet-specific key phrases, which guide llms to focus on both majority and minority opinions. meanwhile, the number of facets is used to adjust the summary length. together with a task-related template, this forms the format guidance to organize opinions and reduce redundancy. to validate the effectiveness of our method, we evaluate the generated summaries using multiple metrics covering different dimensions. experimental results on two social domain datasets demonstrate that our method achieves state-of-theart (sota) performance. facsum can also improve the performance of multiple llms with varying model sizes, and the facsum based on the claude model even achieves new sota results. furthermore, we apply our method to the summa-rization task on paper reviews, and the results indicate that facsum generalizes well to the professional domain.