MILES: A Milestone-Centric Framework for LLM-Driven News Timeline Summarization

A news timeline must preserve the events that explain how a topic develops, even when those events are buried in articles about something else. This makes timeline summarization a problem of retaining relevant events and selecting the final milestones. We introduce MILES (MIlestone selection with LLM-guided Evidence Structuring), a training-free framework for topic-focused timeline summarization (Topic-TLS) from a fixed collection of news articles. Topic-aware multi-event extraction retains both article-main events and embedded topic milestones; date-cluster shortlisting exposes a bounded candidate set; and an LLM selects the final milestones by their semantic importance. On Entities, Crisis, and T17, MILES achieves the highest semantic-alignment F1 among the evaluated systems, improving over LLM-TLS by 35.6%, 33.3%, and 64.1%, respectively. Controlled experiments locate the benefit: on identical candidate pools, every tested LLM selector outperforms the mechanical alternatives, while extraction and shortlisting determine which milestones remain available to select. Human preference studies and a diagnostic with four LLM judges support the semantic evaluation. These results support retaining topic events and using an LLM to select milestones from a bounded candidate pool.