Enterprise AI Analysis
EvioSum: An Evidence-Guided Generation Framework for Faithful and Interpretable Opinion Summarization
A deep dive into the latest advancements in AI, and how they apply to your enterprise.
By JIAN WANG, YANJIE LIANG, YUQING SUN, BIN GONG | Published: 21 February 2026
Executive Impact Summary
The faithful and interpretable opinion summarization aims to generate a summary that captures the diverse opinions expressed in a document set while providing explanations for the divergences between these opinions. In this paper, we propose an evidence-guided framework to enhance opinion coverage and provide divergence explanations. It first generates the majority opinion as an initial summary and partitions the source documents into multiple evidence sets based on their relevance to the majority opinion. Then, a summary extension strategy is employed to expand the initial summary by incorporating different opinions from these sets. The framework also employs a submodular optimization algorithm to select evidence from different evidence sets in order to reflect the divergences between opinions. Experiments on two benchmark datasets demonstrate that our method outperforms multiple baselines in terms of both the lexical and semantic consistency with reference summaries, while having low computational overhead. Ablation studies confirm that both the document partition and summary extension mechanisms contribute to the model perfor-mance. The LLM-based and human evaluation results also show that our method can identify more comprehensive evidence that better captures opinion divergences.
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10.1145/3773966.3777962
DOI
21 February 2026
Published
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Methodology
Experimental Results
Interpretability & Case Study
EvioSum Methodology
EvioSum introduces a novel evidence-guided generation framework for opinion summarization. It partitions documents based on majority opinion, extends summaries iteratively, and uses submodular optimization to explain opinion divergences, ensuring faithfulness and interpretability.
Details: The framework first generates a majority opinion and partitions source documents into support, divergent, and neutral evidence sets. It then extends the initial summary by incorporating opinions from these sets. For interpretability, it constructs an explanation set using an aspect-enhanced evaluation method and a greedy submodular optimization algorithm to select evidence pairs reflecting opinion divergences.
Experimental Results Overview
Experiments on MOS datasets (EO and CO) show EvioSum outperforms state-of-the-art methods in ROUGE and BERTScore, with lower computational overhead. Ablation studies confirm the effectiveness of document partition and summary extension.
Details: EvioSum achieves better lexical and semantic consistency than baselines, with a complexity of O(|D|) compared to CPSum's O(|D|²). It also demonstrates robustness across different LLMs like Vicuna-7B, GPT-40-mini, and Claude-3.5-Sonnet, showing significant improvements in ROUGE-1 and ROUGE-L.
Interpretability & Case Study Findings
EvioSum's explanation set accurately reflects opinion divergences, as validated by LLM-based and human evaluations. A case study highlights its ability to organize opinions logically and provide comprehensive evidence for divergences.
Details: Human evaluation indicates EvioSum provides clear articulation, logical coherence, and conciseness, surpassing CPSum and DeepSeek. The explanation set identifies more effective evidence, covering critical aspects like 'climate issues' and 'Oscars platform' more comprehensively than baseline methods.
EvioSum Framework Overview
The EvioSum framework systematically guides LLMs through opinion summarization.
Obtain Topic & Majority Opinion
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Opinion-Coverage Document Partition
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Evidence-Guided Opinion Extension
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Aspect-Enhanced Explanation Set Evaluation
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Divergence-Focused Explanation
Performance Benchmark: EvioSum's Edge
34.21 ROUGE-1 Score (EO Dataset)EvioSum's ROUGE-1 score of 34.21 on the Election Opinionated (EO) dataset demonstrates a strong improvement over state-of-the-art methods like CPSum (33.56) and LLMs such as Claude-3.5-haiku (29.56). This highlights its superior lexical accuracy and recall in generating faithful summaries.
| Feature | EvioSum | Typical LLM-based Methods |
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| Opinion Coverage |
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| Interpretability |
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| Computational Overhead |
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Case Study: Leonardo DiCaprio's Climate Speech
An example demonstrating EvioSum's ability to summarize complex, divergent opinions.
Context: The case revolves around opinions on Leonardo DiCaprio's Oscar acceptance speech, where he raised the issue of climate change. Opinions vary from praise for using his platform to criticism regarding his private jet usage and the 'awkward' integration of the topic.
Majority Opinion: Leonardo DiCaprio's speech on climate change at the Oscars was significant and impactful, bringing attention to a pressing global issue and inspiring action.
Divergent Opinion: Others question DiCaprio's commitment, pointing out private airplane use, skepticism about people taking climate change seriously, or criticizing the integration of climate change rhetoric into speeches. Some believe population growth poses a similar threat.
Explanation Pairs:
Aspect: Climate change as a global issue
Support: I love how Leonardo DiCaprio related his speech back to climate change.
Divergent: There's something awkward about Leonardo DiCaprio trying to shoehorn climate change rhetoric into his speech.
Aspect: Utilizing the Oscars platform for advocacy
Support: Great that Leonardo DiCaprio used the Oscars platform to address climate change.
Divergent: There's something awkward about Leonardo DiCaprio trying to shoehorn climate change rhetoric into his speech.
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