# What's The Latest? A Question-driven News Chatbot

arXiv: https://arxiv.org/abs/2105.05392 · HTML: https://mlsanity.com/paper/2105.05392/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 15 references that are in the map.
- First posted on arXiv 12 May 2021.

## Builds on (most cited first, 8 of 8)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [CoQA: A Conversational Question Answering Challenge](https://mlsanity.com/paper/1808.07042/) (2018)
- [QuAC : Question Answering in Context](https://mlsanity.com/paper/1808.07036/) (2018)
- [NewsQA: A Machine Comprehension Dataset](https://mlsanity.com/paper/1611.09830/) (2016)
- [Learning to Ask: Neural Question Generation for Reading Comprehension](https://mlsanity.com/paper/1705.00106/) (2017)
- [The Summary Loop: Learning to Write Abstractive Summaries Without Examples](https://mlsanity.com/paper/2105.05361/) (2021)
- [Generating Question-Answer Hierarchies](https://mlsanity.com/paper/1906.02622/) (2019)

## Built on by (most cited first, 14 of 14)

- [LLMs Get Lost In Multi-Turn Conversation](https://mlsanity.com/paper/2505.06120/) (2025)
- [MixQG: Neural Question Generation with Mixed Answer Types](https://mlsanity.com/paper/2110.08175/) (2021)
- [Quiz Design Task: Helping Teachers Create Quizzes with Automated Question Generation](https://mlsanity.com/paper/2205.01730/) (2022)
- [Discord Questions: A Computational Approach To Diversity Analysis in News Coverage](https://mlsanity.com/paper/2211.05007/) (2022)
- [Generating Diverse and High-Quality Texts by Minimum Bayes Risk Decoding](https://mlsanity.com/paper/2401.05054/) (2024)
- [NewsPod: Automatic and Interactive News Podcasts](https://mlsanity.com/paper/2202.07146/) (2022)
- [Closed-book Question Generation via Contrastive Learning](https://mlsanity.com/paper/2210.06781/) (2022)
- [Mitigating Lost in Multi-turn Conversation via Curriculum RL with Verifiable Accuracy and Abstention Rewards](https://mlsanity.com/paper/2510.18731/) (2025)
- [Diversity Enhanced Narrative Question Generation for Storybooks](https://mlsanity.com/paper/2310.16446/) (2023)
- [Automated question generation and question answering from Turkish texts](https://mlsanity.com/paper/2111.06476/) (2021)
- [Designing and Evaluating Interfaces that Highlight News Coverage Diversity Using Discord Questions](https://mlsanity.com/paper/2302.08997/) (2023)
- [Generating Self-Contained and Summary-Centric Question Answer Pairs via Differentiable Reward Imitation Learning](https://mlsanity.com/paper/2109.04689/) (2021)
- [On the Evaluation of Answer-Agnostic Paragraph-level Multi-Question Generation](https://mlsanity.com/paper/2203.04464/) (2022)
- [Salespeople vs SalesBot: Exploring the Role of Educational Value in Conversational Recommender Systems](https://mlsanity.com/paper/2310.17749/) (2023)

## Abstract (arXiv)

This work describes an automatic news chatbot that draws content from a diverse set of news articles and creates conversations with a user about the news. Key components of the system include the automatic organization of news articles into topical chatrooms, integration of automatically generated questions into the conversation, and a novel method for choosing which questions to present which avoids repetitive suggestions. We describe the algorithmic framework and present the results of a usability study that shows that news readers using the system successfully engage in multi-turn conversations about specific news stories.