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Agent Newsroom: Efficient Chronological Report Generation via Dynamic Multi-Agent Collaboration

Paper · 2026 · Zhenhua Wang, Chunlei Wang, Yue Geng et al.

Summary

Timeline Summarization (TLS) asks a system to turn a large, timestamped news collection into a dated list of key events for a given query. Current LLM-based TLS methods mostly rely on one model reading long, overlapping contexts. This wastes tokens and time, and the output tends to reproduce the uneven, burst-driven distribution of news coverage. The authors recast TLS as coordinated work among several agents, modeled on an editorial office. In MAS-TLS, a Master Agent divides retrieved documents among specialized sub-agents using a submodular objective that rewards both relevance coverage and spread across time. Each sub-agent selects dates that differ from those of its peers and writes evidence-based event sentences. The master then cross-checks the proposals, merges duplicates and assembles the final timeline. A Bayesian bandit chooses which agents stay active in the next round under a token/time budget. On three standard TLS benchmarks, the system matches or outperforms single-model baselines on semantic coverage and date accuracy at much lower cost. This suggests that multi-agent decomposition is a practical route to cheaper and more balanced event chronologies.

Key points

  • Architecture: The system uses a star topology with four stages: editorial assignment, autonomous investigation, collaborative scrutiny and adaptive orchestration.
    • Document allocation combines a saturated facility-location coverage term, which includes temporal decay, with a diversity term computed over temporal bins (after Lin and Bilmes, 2011).
    • Agent scheduling is handled by a non-stationary Bayesian bandit controller.
  • Cost: Compared with monolithic LLM baselines, token consumption drops by 31%–86% and inference time by 5%–52%.
  • Quality: Reported improvements cover both how much semantic content the timelines capture (AR) and how accurately they pinpoint dates (Date-F1).
    • Generated timelines show temporal pacing and topical balance closer to human-written references.
    • Results hold across different LLM backbones and across languages.
  • Evidence audit: Only about 5.3–7.9% of sampled events are unsupported, depending on the benchmark.
    • Removing the LLM ranking step shifts this rate by under 3%, so the adjudication step does not trade grounding for coverage.
  • Limitations: Experiments are limited to retrospective, news-style benchmarks; streaming, social-media or scientific corpora are not tested.
    • Agent roles and prompts are fixed.
    • The case study visualizes agent activation and reliability scores on the Yemen 2011 timeline.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Huazhong University of Science and TechnologyWuhan · China45 works · 6,345 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

Cite

Cite as Zhenhua Wang, Chunlei Wang, Yue Geng and Bang Wang. 2026. Agent Newsroom: Efficient Chronological Report Generation via Dynamic Multi-Agent Collaboration. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.1149

@inproceedings{wang2026agent,
  title = {{Agent Newsroom: Efficient Chronological Report Generation via Dynamic Multi-Agent Collaboration}},
  author = {Zhenhua Wang, Chunlei Wang, Yue Geng and Bang Wang},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1149},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1149}
}

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