# Identifying Informational Sources in News Articles

arXiv: https://arxiv.org/abs/2305.14904 · HTML: https://mlsanity.com/paper/2305.14904/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 66 references that are in the map.
- First posted on arXiv 24 May 2023.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Big Bird: Transformers for Longer Sequences](https://mlsanity.com/paper/2007.14062/) (2020)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [Learning to Compress Prompts with Gist Tokens](https://mlsanity.com/paper/2304.08467/) (2023)
- [Can Large Language Models Transform Computational Social Science?](https://mlsanity.com/paper/2305.03514/) (2023)
- [Plan-And-Write: Towards Better Automatic Storytelling](https://mlsanity.com/paper/1811.05701/) (2018)
- [A Survey on Stance Detection for Mis- and Disinformation Identification](https://mlsanity.com/paper/2103.00242/) (2021)
- [Coreference Resolution without Span Representations](https://mlsanity.com/paper/2101.00434/) (2021)
- [Neural End-to-End Learning for Computational Argumentation Mining](https://mlsanity.com/paper/1704.06104/) (2017)
- [LingMess: Linguistically Informed Multi Expert Scorers for Coreference Resolution](https://mlsanity.com/paper/2205.12644/) (2022)
- [NewsEdits: A News Article Revision Dataset and a Document-Level Reasoning Challenge](https://mlsanity.com/paper/2206.07106/) (2022)
- [DiscoDVT: Generating Long Text with Discourse-Aware Discrete Variational Transformer](https://mlsanity.com/paper/2110.05999/) (2021)
- [Credibility-based Fake News Detection](https://mlsanity.com/paper/1911.00643/) (2019)
- [Quootstrap: Scalable Unsupervised Extraction of Quotation-Speaker Pairs from Large News Corpora via Bootstrapping](https://mlsanity.com/paper/1804.02525/) (2018)
- ["Don't quote me on that": Finding Mixtures of Sources in News Articles](https://mlsanity.com/paper/2104.09656/) (2021)
- [Sequentially Controlled Text Generation](https://mlsanity.com/paper/2301.02299/) (2023)
- [Quotatives Indicate Decline in Objectivity in U.S. Political News](https://mlsanity.com/paper/2210.15476/) (2022)
- [If it Bleeds, it Leads: A Computational Approach to Covering Crime in Los Angeles](https://mlsanity.com/paper/2206.07115/) (2022)

## Built on by (most cited first, 5 of 5)

- [Tracking the Newsworthiness of Public Documents](https://mlsanity.com/paper/2311.09734/) (2023)
- [TRACE: TRansformer-based Attribution using Contrastive Embeddings in LLMs](https://mlsanity.com/paper/2407.04981/) (2024)
- [Explaining Mixtures of Sources in News Articles](https://mlsanity.com/paper/2411.05192/) (2024)
- [NewsEdits 2.0: Learning the Intentions Behind Updating News](https://mlsanity.com/paper/2411.18811/) (2024)
- [NewsHomepages: Homepage Layouts Capture Information Prioritization Decisions](https://mlsanity.com/paper/2501.00004/) (2024)

## Abstract (arXiv)

News articles are driven by the informational sources journalists use in reporting. Modeling when, how and why sources get used together in stories can help us better understand the information we consume and even help journalists with the task of producing it. In this work, we take steps toward this goal by constructing the largest and widest-ranging annotated dataset, to date, of informational sources used in news writing. We show that our dataset can be used to train high-performing models for information detection and source attribution. We further introduce a novel task, source prediction, to study the compositionality of sources in news articles. We show good performance on this task, which we argue is an important proof for narrative science exploring the internal structure of news articles and aiding in planning-based language generation, and an important step towards a source-recommendation system to aid journalists.