# Seeing Things from a Different Angle: Discovering Diverse Perspectives about Claims

arXiv: https://arxiv.org/abs/1906.03538 · HTML: https://mlsanity.com/paper/1906.03538/

## Key facts

- Cited by 74 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2019.

## Built on by (most cited first, 30 of 74)

- [FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation](https://mlsanity.com/paper/2305.14251/) (2023)
- [Evaluating Verifiability in Generative Search Engines](https://mlsanity.com/paper/2304.09848/) (2023)
- [Rich Knowledge Sources Bring Complex Knowledge Conflicts: Recalibrating Models to Reflect Conflicting Evidence](https://mlsanity.com/paper/2210.13701/) (2022)
- [Goal Driven Discovery of Distributional Differences via Language Descriptions](https://mlsanity.com/paper/2302.14233/) (2023)
- [GEO: Generative Engine Optimization](https://mlsanity.com/paper/2311.09735/) (2023)
- [Automated Fact-Checking: A Survey](https://mlsanity.com/paper/2109.11427/) (2021)
- [CLINE: Contrastive Learning with Semantic Negative Examples for Natural Language Understanding](https://mlsanity.com/paper/2107.00440/) (2021)
- [AmbiFC: Fact-Checking Ambiguous Claims with Evidence](https://mlsanity.com/paper/2104.00640/) (2021)
- [Cross-Domain Label-Adaptive Stance Detection](https://mlsanity.com/paper/2104.07467/) (2021)
- [Trends in Integration of Knowledge and Large Language Models: A Survey and Taxonomy of Methods, Benchmarks, and Applications](https://mlsanity.com/paper/2311.05876/) (2023)
- [Persuade Me if You Can: A Framework for Evaluating Persuasion Effectiveness and Susceptibility Among Large Language Models](https://mlsanity.com/paper/2503.01829/) (2025)
- [Overview of the 2021 Key Point Analysis Shared Task](https://mlsanity.com/paper/2110.10577/) (2021)
- [IAM: A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks](https://mlsanity.com/paper/2203.12257/) (2022)
- [Sub-Sentence Encoder: Contrastive Learning of Propositional Semantic Representations](https://mlsanity.com/paper/2311.04335/) (2023)
- [Open Domain Question Answering with Conflicting Contexts](https://mlsanity.com/paper/2410.12311/) (2024)
- [Do Differences in Values Influence Disagreements in Online Discussions?](https://mlsanity.com/paper/2310.15757/) (2023)
- [Large Language Models in Argument Mining: A Survey](https://mlsanity.com/paper/2506.16383/) (2025)
- [MultiOpEd: A Corpus of Multi-Perspective News Editorials](https://mlsanity.com/paper/2106.02725/) (2021)
- [Probing Multilingual Language Models for Discourse](https://mlsanity.com/paper/2106.04832/) (2021)
- [Beyond Relevance: Evaluate and Improve Retrievers on Perspective Awareness](https://mlsanity.com/paper/2405.02714/) (2024)
- [The Nature of NLP: Analyzing Contributions in NLP Papers](https://mlsanity.com/paper/2409.19505/) (2024)
- [Both Matter: Enhancing the Emotional Intelligence of Large Language Models without Compromising the General Intelligence](https://mlsanity.com/paper/2402.10073/) (2024)
- [Open-World Evaluation for Retrieving Diverse Perspectives](https://mlsanity.com/paper/2409.18110/) (2024)
- [Towards Strategic Persuasion with Language Models](https://mlsanity.com/paper/2509.22989/) (2025)
- [An Empirical Analysis of Diversity in Argument Summarization](https://mlsanity.com/paper/2402.01535/) (2024)
- [Voices in a Crowd: Searching for Clusters of Unique Perspectives](https://mlsanity.com/paper/2407.14259/) (2024)
- [Large Language Models for Subjective Language Understanding: A Survey](https://mlsanity.com/paper/2508.07959/) (2025)
- [Stanceosaurus: Classifying Stance Towards Multilingual Misinformation](https://mlsanity.com/paper/2210.15954/) (2022)
- [Design Challenges for a Multi-Perspective Search Engine](https://mlsanity.com/paper/2112.08357/) (2021)
- [OpenStance: Real-world Zero-shot Stance Detection](https://mlsanity.com/paper/2210.14299/) (2022)

## Abstract (arXiv)

One key consequence of the information revolution is a significant increase and a contamination of our information supply. The practice of fact checking won't suffice to eliminate the biases in text data we observe, as the degree of factuality alone does not determine whether biases exist in the spectrum of opinions visible to us. To better understand controversial issues, one needs to view them from a diverse yet comprehensive set of perspectives. For example, there are many ways to respond to a claim such as "animals should have lawful rights", and these responses form a spectrum of perspectives, each with a stance relative to this claim and, ideally, with evidence supporting it. Inherently, this is a natural language understanding task, and we propose to address it as such. Specifically, we propose the task of substantiated perspective discovery where, given a claim, a system is expected to discover a diverse set of well-corroborated perspectives that take a stance with respect to the claim. Each perspective should be substantiated by evidence paragraphs which summarize pertinent results and facts. We construct PERSPECTRUM, a dataset of claims, perspectives and evidence, making use of online debate websites to create the initial data collection, and augmenting it using search engines in order to expand and diversify our dataset. We use crowd-sourcing to filter out noise and ensure high-quality data. Our dataset contains 1k claims, accompanied with pools of 10k and 8k perspective sentences and evidence paragraphs, respectively. We provide a thorough analysis of the dataset to highlight key underlying language understanding challenges, and show that human baselines across multiple subtasks far outperform ma-chine baselines built upon state-of-the-art NLP techniques. This poses a challenge and opportunity for the NLP community to address.