@inproceedings{wang-etal-2026-agent,
    title = "Agent Newsroom: Efficient Chronological Report Generation via Dynamic Multi-Agent Collaboration",
    author = "Wang, Zhenhua  and
      Wang, Chunlei  and
      Geng, Yue  and
      Wang, Bang",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1149/",
    pages = "25063--25084",
    ISBN = "979-8-89176-390-6",
    abstract = "Many real-world applications require generating a chronological report from an evolving document stream; Timeline Summarization (TLS) provides a standard testbed for this setting. While large language models (LLMs) improve event synthesis, most LLM-based TLS systems remain monolithic: they repeatedly process overlapping evidence and often mirror the corpus' bursty reporting patterns, producing redundant timelines with temporal/topical imbalance and high cost. We propose **MAS-TLS**, a multi-agent framework that casts TLS as a *newsroom-like* collaboration. A master editor steers balanced coverage by allocating system-visible evidence with a coverage{--}diversity objective; specialist reporter agents independently draft time-anchored, evidence-grounded events while cross-reviewing to limit redundancy; an adjudication round reconciles competing drafts and consolidates duplicates into a global timeline; and a non-stationary Bayesian controller adaptively staffs agents under token/time budgets. Experiments on three benchmarks show that MAS-TLS improves semantic coverage and temporal grounding while substantially reducing token usage and latency."
}