# Design Challenges for a Multi-Perspective Search Engine

arXiv: https://arxiv.org/abs/2112.08357 · HTML: https://mlsanity.com/paper/2112.08357/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 44 references that are in the map.
- First posted on arXiv 15 Dec 2021.

## Builds on (most cited first, 17 of 17)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://mlsanity.com/paper/1705.03551/) (2017)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation](https://mlsanity.com/paper/1708.00055/) (2017)
- [On Faithfulness and Factuality in Abstractive Summarization](https://mlsanity.com/paper/2005.00661/) (2020)
- [Reading Wikipedia to Answer Open-Domain Questions](https://mlsanity.com/paper/1704.00051/) (2017)
- [SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine](https://mlsanity.com/paper/1704.05179/) (2017)
- [Decontextualization: Making Sentences Stand-Alone](https://mlsanity.com/paper/2102.05169/) (2021)
- [End-to-End Neural Ad-hoc Ranking with Kernel Pooling](https://mlsanity.com/paper/1706.06613/) (2017)
- [Seeing Things from a Different Angle: Discovering Diverse Perspectives about Claims](https://mlsanity.com/paper/1906.03538/) (2019)
- [From Arguments to Key Points: Towards Automatic Argument Summarization](https://mlsanity.com/paper/2005.01619/) (2020)
- [QED: A Framework and Dataset for Explanations in Question Answering](https://mlsanity.com/paper/2009.06354/) (2020)
- [Multi-step Retriever-Reader Interaction for Scalable Open-domain Question Answering](https://mlsanity.com/paper/1905.05733/) (2019)
- [Corpus Wide Argument Mining -- a Working Solution](https://mlsanity.com/paper/1911.10763/) (2019)
- [MultiOpEd: A Corpus of Multi-Perspective News Editorials](https://mlsanity.com/paper/2106.02725/) (2021)
- [PerspectroScope: A Window to the World of Diverse Perspectives](https://mlsanity.com/paper/1906.04761/) (2019)

## Built on by (most cited first, 7 of 7)

- [Open Domain Question Answering with Conflicting Contexts](https://mlsanity.com/paper/2410.12311/) (2024)
- [Open-World Evaluation for Retrieving Diverse Perspectives](https://mlsanity.com/paper/2409.18110/) (2024)
- [Improving Neutral Point-of-View Generation with Data- and Parameter-Efficient RL](https://mlsanity.com/paper/2503.03654/) (2025)
- [Facilitating Opinion Diversity through Hybrid NLP Approaches](https://mlsanity.com/paper/2405.09439/) (2024)
- [Conflicts in Texts: Data, Implications and Challenges](https://mlsanity.com/paper/2504.19472/) (2025)
- [Inter-Passage Verification for Multi-evidence Multi-answer QA](https://mlsanity.com/paper/2506.00425/) (2025)
- [ConflictScore: Identifying and Measuring How Language Models Handle Conflicting Evidence](https://mlsanity.com/paper/2606.26437/) (2026)

## Abstract (arXiv)

Many users turn to document retrieval systems (e.g. search engines) to seek answers to controversial questions. Answering such user queries usually require identifying responses within web documents, and aggregating the responses based on their different perspectives. Classical document retrieval systems fall short at delivering a set of direct and diverse responses to the users. Naturally, identifying such responses within a document is a natural language understanding task. In this paper, we examine the challenges of synthesizing such language understanding objectives with document retrieval, and study a new perspective-oriented document retrieval paradigm. We discuss and assess the inherent natural language understanding challenges in order to achieve the goal. Following the design challenges and principles, we demonstrate and evaluate a practical prototype pipeline system. We use the prototype system to conduct a user survey in order to assess the utility of our paradigm, as well as understanding the user information needs for controversial queries.