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

TL;DR

This paper introduces CHRONOS, an iterative self-questioning approach using LLMs for open-domain news timeline summarization, effectively constructing coherent timelines from large, diverse news content.

Contribution

It presents a novel iterative self-questioning method with a new dataset, Open-TLS, for evaluating open-domain timeline summarization with LLMs.

Findings

CHRONOS effectively summarizes open-domain news timelines.

It rivals state-of-the-art closed-domain systems.

The Open-TLS dataset enables comprehensive evaluation.

Abstract

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.