# Unfolding the Headline: Iterative Self-Questioning for News Retrieval and Timeline Summarization

arXiv: https://arxiv.org/abs/2501.00888 · HTML: https://mlsanity.com/paper/2501.00888/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jan 2025.

## Built on by (most cited first, 13 of 13)

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- [UniDoc-RL: Coarse-to-Fine Visual RAG with Hierarchical Actions and Dense Rewards](https://mlsanity.com/paper/2604.14967/) (2026)
- [ThreadSumm: Summarization of Nested Discourse Threads Using Tree of Thoughts](https://mlsanity.com/paper/2604.17648/) (2026)
- [Large Language Model-Powered Query-Driven Event Timeline Summarization in Industrial Search](https://mlsanity.com/paper/2605.27066/) (2026)
- [NTS-CoT: Mitigating Hallucinations in LLM-based News Timeline Summarization with Chain-of-Thought Reasoning](https://mlsanity.com/paper/2606.13171/) (2026)

## Abstract (arXiv)

In the fast-changing realm of information, the capacity to construct coherent timelines from extensive event-related content has become increasingly significant and challenging. The complexity arises in aggregating related documents to build a meaningful event graph around a central topic. This paper proposes CHRONOS - Causal Headline Retrieval for Open-domain News Timeline SummarizatiOn via Iterative Self-Questioning, which offers a fresh perspective on the integration of Large Language Models (LLMs) to tackle the task of Timeline Summarization (TLS). By iteratively reflecting on how events are linked and posing new questions regarding a specific news topic to gather information online or from an offline knowledge base, LLMs produce and refresh chronological summaries based on documents retrieved in each round. Furthermore, we curate Open-TLS, a novel dataset of timelines on recent news topics authored by professional journalists to evaluate open-domain TLS where information overload makes it impossible to find comprehensive relevant documents from the web. Our experiments indicate that CHRONOS is not only adept at open-domain timeline summarization, but it also rivals the performance of existing state-of-the-art systems designed for closed-domain applications, where a related news corpus is provided for summarization.