# NewsQuote: A Dataset Built on Quote Extraction and Attribution for Expert Recommendation in Fact-Checking

arXiv: https://arxiv.org/abs/2305.04825 · HTML: https://mlsanity.com/paper/2305.04825/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 33 references that are in the map.
- First posted on arXiv 5 May 2023.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- [Simple BERT Models for Relation Extraction and Semantic Role Labeling](https://mlsanity.com/paper/1904.05255/) (2019)
- [Automated Fact-Checking: A Survey](https://mlsanity.com/paper/2109.11427/) (2021)
- [DirectQuote: A Dataset for Direct Quotation Extraction and Attribution in News Articles](https://mlsanity.com/paper/2110.07827/) (2021)
- [Some Observations on Fact-Checking Work with Implications for Computational Support](https://mlsanity.com/paper/2305.02224/) (2023)
- [Expert Finding in Legal Community Question Answering](https://mlsanity.com/paper/2201.07667/) (2022)
- [Expert Finding in Community Question Answering: A Review](https://mlsanity.com/paper/1804.07958/) (2018)
- [Neural Duplicate Question Detection without Labeled Training Data](https://mlsanity.com/paper/1911.05594/) (2019)
- [PANACEA: An Automated Misinformation Detection System on COVID-19](https://mlsanity.com/paper/2303.01241/) (2023)

## Abstract (arXiv)

To enhance the ability to find credible evidence in news articles, we propose a novel task of expert recommendation, which aims to identify trustworthy experts on a specific news topic. To achieve the aim, we describe the construction of a novel NewsQuote dataset consisting of 24,031 quote-speaker pairs that appeared on a COVID-19 news corpus. We demonstrate an automatic pipeline for speaker and quote extraction via a BERT-based Question Answering model. Then, we formulate expert recommendations as document retrieval task by retrieving relevant quotes first as an intermediate step for expert identification, and expert retrieval by directly retrieving sources based on the probability of a query conditional on a candidate expert. Experimental results on NewsQuote show that document retrieval is more effective in identifying relevant experts for a given news topic compared to expert retrieval