---
title: "Adaptive Planning for Multi-Attribute Controllable Summarization"
description: "The authors propose PACO, a training-free method that treats control as a planning problem. 58 references."
authors: ["Sangwon Ryu", "Heejin Do", "Yunsu Kim", "Gary Lee", "Jungseul Ok"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.845/"
doi: "10.18653/v1/2026.acl-long.845"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/ryu-2026-adaptive-planning-multi-attribute-controllable/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/ryu-2026-adaptive-planning-multi-attribute-controllable/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/21/) / Adaptive Planning for Multi-Attribute Controllable Summarization with Monte Carlo Tree Search

# Adaptive Planning for Multi-Attribute Controllable Summarization with Monte Carlo Tree Search

**Authors:** [Sangwon Ryu](https://md.research.elamur.ai/free/en/authors/sangwon-ryu/), [Heejin Do](https://md.research.elamur.ai/free/en/authors/heejin-do/), [Yunsu Kim](https://md.research.elamur.ai/free/en/authors/yunsu-kim/), [Gary Lee](https://md.research.elamur.ai/free/en/authors/gary-lee/) and [Jungseul Ok](https://md.research.elamur.ai/free/en/authors/jungseul-ok/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.845/) · [PDF](https://aclanthology.org/2026.acl-long.845.pdf) · [DOI 10.18653/v1/2026.acl-long.845](https://doi.org/10.18653/v1/2026.acl-long.845) · [OpenAlex](https://openalex.org/W7166835640)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
The paper tackles multi-attribute controllable summarization, where a summary must meet several user constraints at once: extractiveness, length, specificity, topic and speaker. These attributes interact. For example, raising extractiveness can break the length target. Earlier systems, such as mixture-of-experts decoders and prompt/prefix tuning, need fine-tuning for each attribute.

The authors propose PACO, a training-free method that treats control as a planning problem. It starts from an LLM summary prompted with all constraints. A customized Monte Carlo Tree Search then decides which single attribute to fix next. Each tree node is a complete summary, and each action adjusts one attribute. Attributes may be revisited, and the best-controlled summary anywhere in the tree is returned.

Experiments cover MACSum Doc, MACSum Dial and DialogSum with LLMs of several sizes. PACO beats LLM self-planning and fine-tuned baselines without hurting summary quality. With Llama-3.2-1B it matches Llama-3.3-70B baselines. This matters because strong control is possible without attribute-specific training, though search adds runtime cost.

## Key points
- **Search design:**
  - Action space: {ext, len, spc, top, spk}.
  - Node selection uses a PUCT-style rule.
  - The policy model sees the full history of earlier summaries when making each edit.
- **Scoring:**
  - Rewards are based on the mean absolute deviation from targets.
  - Extractiveness, length and specificity are "deterministic" attributes that must hit exact targets.
  - Topic and speaker are scored by alignment, where higher is better; speaker alignment uses BERTScore.
- **Reward ablation:** Using only the local reward worked as well as adding a heuristic look-ahead reward, at lower cost. The heuristic signal helped little, especially for the 1B model.
- **Attribute analysis:** Length was the attribute adjusted most often. Larger models kept the initial summary less often and edited extractiveness and specificity more.
- **Main limitation:** The tree search is computationally expensive, although PACO works entirely at test time.

*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

- [ETH Zurich](https://md.research.elamur.ai/free/en/universities/eth-zurich/) — University, Zurich, Switzerland · 72 works · 5,176 citations
- [Pohang University of Science and Technology](https://md.research.elamur.ai/free/en/universities/pohang-university-of-science-and-technology/) — University, Pohang, South Korea · 7 works · 464 citations
- [Lega Italiana per la Lotta ai Tumori](https://md.research.elamur.ai/free/en/ngos/lega-italiana-per-la-lotta-ai-tumori/) — Nonprofit, Rome, Italy · 2 works · 2 citations

📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 16,039 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 15,891 works

🧭 Topics (1): [📝 Summarization](https://md.research.elamur.ai/free/en/topics/summarization/) · 232 works

## Cite

Cite as: Sangwon Ryu, Heejin Do, Yunsu Kim, Gary Lee and Jungseul Ok. 2026. Adaptive Planning for Multi-Attribute Controllable Summarization with Monte Carlo Tree Search. 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.

```bibtex
@inproceedings{ryu2026adaptive,
  title = {{Adaptive Planning for Multi-Attribute Controllable Summarization with Monte Carlo Tree Search}},
  author = {Sangwon Ryu, Heejin Do, Yunsu Kim, Gary Lee and Jungseul Ok},
  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.845},
  url = {https://doi.org/10.18653/v1/2026.acl-long.845}
}
```

- [← previous Adaptive Emotion Management in Human-robot Dialogue using Online Group Relative Policy Optimization](https://md.research.elamur.ai/free/en/papers/manaseryan-2026-adaptive-emotion-management-human-robot/)
- [next → Adaptive Text2GQL: Integrating Structural Twig Linking and Evolutionary In-Context Learning](https://md.research.elamur.ai/free/en/papers/niu-2026-adaptive-text2gql-integrating-structural-twig/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/21/)

## References

58 references, 18 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Ojas Ahuja, Jiacheng Xu, Akshay Gupta, Kevin Horecka, and Greg Durrett. 2022. ASPECTNEWS: Aspect-oriented summarization of news documents. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6494–6506, Dublin, Ireland. Association… [find](https://scholar.google.com/scholar?q=Ojas+Ahuja%2C+Jiacheng+Xu%2C+Akshay+Gupta%2C+Kevin+Horecka%2C+and+Greg+Durrett%2E+2022%2E+ASPECTNEWS%3A+Aspect%2Doriented+summarization+of+news+documents%2E+In+Proceedings+of+the+60th+Annual+Meeting+of+the+Association+)
2. Alan Akbik, Tanja Bergmann, Duncan Blythe, Kashif Rasul, Stefan Schweter, and Roland Vollgraf. 2019. [FLAIR: An easy-to-use framework for state-of-the-art NLP](https://md.research.elamur.ai/free/en/papers/akbik-2019-flair-easy-use-framework-state/). In NAACL 2019, 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations), pages…
3. Seyed Ali Bahrainian, Sheridan Feucht, and Carsten Eickhoff. 2022. NEWTS: A corpus for news topic-focused summarization. In Findings of the Association for Computational Linguistics: ACL 2022, pages 493–503, Dublin, Ireland. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Seyed+Ali+Bahrainian%2C+Sheridan+Feucht%2C+and+Carsten+Eickhoff%2E+2022%2E+NEWTS%3A+A+corpus+for+news+topic%2Dfocused+summarization%2E+In+Findings+of+the+Association+for+Computational+Linguistics%3A+ACL+2022%2C+pages+4)
4. Jinze Bai et al. 2023. [Qwen technical report](https://md.research.elamur.ai/free/en/papers/bai-2023-qwen-technical-report/). Preprint, arXiv:2309.16609.
5. Cameron B. Browne, Edward Powley, Daniel Whitehouse, Simon M. Lucas, Peter I. Cowling, Philipp Rohlfshagen, Stephen Tavener, Diego Perez, Spyridon Samothrakis, and Simon Colton. 2012. A survey of monte carlo tree search methods. IEEE Transactions on Computational Intelligence and AI in Games, 4(1):1… [find](https://scholar.google.com/scholar?q=Cameron+B%2E+Browne%2C+Edward+Powley%2C+Daniel+Whitehouse%2C+Simon+M%2E+Lucas%2C+Peter+I%2E+Cowling%2C+Philipp+Rohlfshagen%2C+Stephen+Tavener%2C+Diego+Perez%2C+Spyridon+Samothrakis%2C+and+Simon+Colton%2E+2012%2E+A+survey+of+mont)
6. Jean Carletta, Simone Ashby, Sebastien Bourban, Mike Flynn, Mael Guillemot, Thomas Hain, Jaroslav Kadlec, Vasilis Karaiskos, Wessel Kraaij, Melissa Kronenthal, Guillaume Lathoud, Mike Lincoln, Agnes Lisowska, Iain McCowan, Wilfried Post, Dennis Reidsma, and Pierre Wellner. 2005. The ami meeting corp… [find](https://scholar.google.com/scholar?q=Jean+Carletta%2C+Simone+Ashby%2C+Sebastien+Bourban%2C+Mike+Flynn%2C+Mael+Guillemot%2C+Thomas+Hain%2C+Jaroslav+Kadlec%2C+Vasilis+Karaiskos%2C+Wessel+Kraaij%2C+Melissa+Kronenthal%2C+Guillaume+Lathoud%2C+Mike+Lincoln%2C+Agnes+L)
7. Hou Pong Chan, Lu Wang, and Irwin King. 2021. [Controllable summarization with constrained Markov decision process](https://md.research.elamur.ai/free/en/papers/chan-2021-controllable-summarization-constrained-markov-decision/). Transactions of the Association for Computational Linguistics, 9:1213–1232.
8. Zi-Yi Dou, Pengfei Liu, Hiroaki Hayashi, Zhengbao Jiang, and Graham Neubig. 2021. [GSum: A general framework for guided neural abstractive summarization](https://md.research.elamur.ai/free/en/papers/dou-2021-gsum-general-framework-guided-neural/). In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies,…
9. Angela Fan, David Grangier, and Michael Auli. 2018. [Controllable abstractive summarization](https://md.research.elamur.ai/free/en/papers/fan-2018-controllable-abstractive-summarization/). In Proceedings of the 2nd Workshop on Neural Machine Translation and Generation, pages 45–54, Melbourne, Australia. Association for Computational Linguistics.
10. Sylvain Gelly and David Silver. 2011. Monte-carlo tree search and rapid action value estimation in computer go. Artif. Intell., 175:1856–1875. [find](https://scholar.google.com/scholar?q=Sylvain+Gelly+and+David+Silver%2E+2011%2E+Monte%2Dcarlo+tree+search+and+rapid+action+value+estimation+in+computer+go%2E+Artif%2E+Intell%2E%2C+175%3A1856%E2%80%931875%2E)
11. Tanya Goyal, Nazneen Rajani, Wenhao Liu, and Wojciech Kryscinski. 2022. HydraSum: Disentangling style features in text summarization with multi-decoder models. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 464–479, Abu Dhabi, United Arab Emirates. A… [find](https://scholar.google.com/scholar?q=Tanya+Goyal%2C+Nazneen+Rajani%2C+Wenhao+Liu%2C+and+Wojciech+Kryscinski%2E+2022%2E+HydraSum%3A+Disentangling+style+features+in+text+summarization+with+multi%2Ddecoder+models%2E+In+Proceedings+of+the+2022+Conference+on)
12. Tanya Goyal et al. 2023. News summarization and evaluation in the era of gpt-3. Preprint, arXiv:2209.12356. [find](https://scholar.google.com/scholar?q=Tanya+Goyal+et+al%2E+2023%2E+News+summarization+and+evaluation+in+the+era+of+gpt%2D3%2E+Preprint%2C+arXiv%3A2209%2E12356%2E)
13. Aaron Grattafiori et al. 2024. [The llama 3 herd of models](https://md.research.elamur.ai/free/en/papers/grattafiori-2024-llama-3-herd-models/). Preprint, arXiv:2407.21783.
14. Yuxuan Gu, Wenjie Wang, Xiaocheng Feng, Weihong Zhong, Kun Zhu, Lei Huang, Ting Liu, Bing Qin, and Tat-Seng Chua. 2025. Length controlled generation for black-box LLMs. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 16878–168… [find](https://scholar.google.com/scholar?q=Yuxuan+Gu%2C+Wenjie+Wang%2C+Xiaocheng+Feng%2C+Weihong+Zhong%2C+Kun+Zhu%2C+Lei+Huang%2C+Ting+Liu%2C+Bing+Qin%2C+and+Tat%2DSeng+Chua%2E+2025%2E+Length+controlled+generation+for+black%2Dbox+LLMs%2E+In+Proceedings+of+the+63rd+Annu)
15. Shibo Hao, Yi Gu, Haodi Ma, Joshua Hong, Zhen Wang, Daisy Wang, and Zhiting Hu. 2023. Reasoning with language model is planning with world model. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 8154–8173, Singapore. Association for Computational Lingu… [find](https://scholar.google.com/scholar?q=Shibo+Hao%2C+Yi+Gu%2C+Haodi+Ma%2C+Joshua+Hong%2C+Zhen+Wang%2C+Daisy+Wang%2C+and+Zhiting+Hu%2E+2023%2E+Reasoning+with+language+model+is+planning+with+world+model%2E+In+Proceedings+of+the+2023+Conference+on+Empirical+Met)
16. Guoxin Chen, Minpeng Liao, Chengxi Li, and Kai Fan. 2024. [Alphamath almost zero: Process supervision without process](https://md.research.elamur.ai/free/en/papers/chen-2024-alphamath-almost-zero-process-supervision/). In Advances in Neural Information Processing Systems, volume 37, pages 27689–27724. Curran Associates, Inc.
17. Junxian He, Wojciech Kryscinski, Bryan McCann, Nazneen Rajani, and Caiming Xiong. 2022. [CTRLsum: Towards generic controllable text summarization](https://md.research.elamur.ai/free/en/papers/he-2022-ctrlsum-generic-controllable-text-summarization/). In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 5879–5915, Abu Dhabi, United Arab Emirates. Association f…
18. Yulong Chen, Yang Liu, Liang Chen, and Yue Zhang. 2021. [DialogSum: A real-life scenario dialogue summarization dataset](https://md.research.elamur.ai/free/en/papers/chen-2021-dialogsum-real-life-scenario-dialogue/). In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 5062–5074, Online. Association for Computational Linguistics.
19. A. Janin, D. Baron, J. Edwards, D. Ellis, D. Gelbart, N. Morgan, B. Peskin, T. Pfau, E. Shriberg, A. Stolcke, and C. Wooters. 2003. The icsi meeting corpus. In 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP ’03)., volume 1, pages I–I. [find](https://scholar.google.com/scholar?q=A%2E+Janin%2C+D%2E+Baron%2C+J%2E+Edwards%2C+D%2E+Ellis%2C+D%2E+Gelbart%2C+N%2E+Morgan%2C+B%2E+Peskin%2C+T%2E+Pfau%2C+E%2E+Shriberg%2C+A%2E+Stolcke%2C+and+C%2E+Wooters%2E+2003%2E+The+icsi+meeting+corpus%2E+In+2003+IEEE+International+Conference+on+Ac)
20. Levente Kocsis and Csaba Szepesvári. 2006. Bandit based monte-carlo planning. In Proceedings of the 17th European Conference on Machine Learning, ECML’06, page 282–293, Berlin, Heidelberg. Springer-Verlag. [find](https://scholar.google.com/scholar?q=Levente+Kocsis+and+Csaba+Szepesv%C3%A1ri%2E+2006%2E+Bandit+based+monte%2Dcarlo+planning%2E+In+Proceedings+of+the+17th+European+Conference+on+Machine+Learning%2C+ECML%E2%80%9906%2C+page+282%E2%80%93293%2C+Berlin%2C+Heidelberg%2E+Springer%2DVe)
21. Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022. [Large language models are zero-shot reasoners](https://md.research.elamur.ai/free/en/papers/kojima-2022-large-language-models-zero-shot/). In Proceedings of the 36th International Conference on Neural Information Processing Systems, NIPS ’22, Red Hook, NY, USA. Curran Associates Inc.
22. Sungjae Lee, Hyejin Park, Jaechang Kim, and Jungseul Ok. 2025. Semantic exploration with adaptive gating for efficient problem solving with language models. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 588–606, Vienna, Aust… [find](https://scholar.google.com/scholar?q=Sungjae+Lee%2C+Hyejin+Park%2C+Jaechang+Kim%2C+and+Jungseul+Ok%2E+2025%2E+Semantic+exploration+with+adaptive+gating+for+efficient+problem+solving+with+language+models%2E+In+Proceedings+of+the+63rd+Annual+Meeting+o)
23. Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2020. [BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension](https://md.research.elamur.ai/free/en/papers/lewis-2020-bart-denoising-sequence-sequence-pre/). In Proceedings of the 58th Annual Meeting of the…
24. Xiang Lisa Li and Percy Liang. 2021. Prefix-tuning: Optimizing continuous prompts for generation. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 4582–… [find](https://scholar.google.com/scholar?q=Xiang+Lisa+Li+and+Percy+Liang%2E+2021%2E+Prefix%2Dtuning%3A+Optimizing+continuous+prompts+for+generation%2E+In+Proceedings+of+the+59th+Annual+Meeting+of+the+Association+for+Computational+Linguistics+and+the+11t)
25. Chin-Yew Lin. 2004. [ROUGE: A package for automatic evaluation of summaries](https://md.research.elamur.ai/free/en/papers/lin-2004-rouge-package-automatic-evaluation-summaries/). In Text Summarization Branches Out, pages 74–81, Barcelona, Spain. Association for Computational Linguistics.
26. Yizhu Liu, Qi Jia, and Kenny Zhu. 2022. Length control in abstractive summarization by pretraining information selection. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6885–6895, Dublin, Ireland. Association for Computationa… [find](https://scholar.google.com/scholar?q=Yizhu+Liu%2C+Qi+Jia%2C+and+Kenny+Zhu%2E+2022%2E+Length+control+in+abstractive+summarization+by+pretraining+information+selection%2E+In+Proceedings+of+the+60th+Annual+Meeting+of+the+Association+for+Computational)
27. Zhengyuan Liu and Nancy Chen. 2021. Controllable neural dialogue summarization with personal named entity planning. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 92–106, Online and Punta Cana, Dominican Republic. Association for Computational Lingui… [find](https://scholar.google.com/scholar?q=Zhengyuan+Liu+and+Nancy+Chen%2E+2021%2E+Controllable+neural+dialogue+summarization+with+personal+named+entity+planning%2E+In+Proceedings+of+the+2021+Conference+on+Empirical+Methods+in+Natural+Language+Proce)
28. Mounica Maddela, Mayank Kulkarni, and Daniel Preotiuc-Pietro. 2022. EntSUM: A data set for entity-centric extractive summarization. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3355–3366, Dublin, Ireland. Association for Co… [find](https://scholar.google.com/scholar?q=Mounica+Maddela%2C+Mayank+Kulkarni%2C+and+Daniel+Preotiuc%2DPietro%2E+2022%2E+EntSUM%3A+A+data+set+for+entity%2Dcentric+extractive+summarization%2E+In+Proceedings+of+the+60th+Annual+Meeting+of+the+Association+for+Com)
29. Ziming Mao, Chen Henry Wu, Ansong Ni, Yusen Zhang, Rui Zhang, Tao Yu, Budhaditya Deb, Chenguang Zhu, Ahmed Awadallah, and Dragomir Radev. 2022. DYLE: Dynamic latent extraction for abstractive long-input summarization. In Proceedings of the 60th Annual Meeting of the Association for Computational Lin… [find](https://scholar.google.com/scholar?q=Ziming+Mao%2C+Chen+Henry+Wu%2C+Ansong+Ni%2C+Yusen+Zhang%2C+Rui+Zhang%2C+Tao+Yu%2C+Budhaditya+Deb%2C+Chenguang+Zhu%2C+Ahmed+Awadallah%2C+and+Dragomir+Radev%2E+2022%2E+DYLE%3A+Dynamic+latent+extraction+for+abstractive+long%2Dinp)
30. Dhruv Mehra, Lingjue Xie, Ella Hofmann-Coyle, Mayank Kulkarni, and Daniel Preotiuc-Pietro. 2023. EntSUMv2: Dataset, models and evaluation for more abstractive entity-centric summarization. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 5538–5547, Sin… [find](https://scholar.google.com/scholar?q=Dhruv+Mehra%2C+Lingjue+Xie%2C+Ella+Hofmann%2DCoyle%2C+Mayank+Kulkarni%2C+and+Daniel+Preotiuc%2DPietro%2E+2023%2E+EntSUMv2%3A+Dataset%2C+models+and+evaluation+for+more+abstractive+entity%2Dcentric+summarization%2E+In+Proceedi)
31. Artidoro Pagnoni, Alex Fabbri, Wojciech Kryscinski, and Chien-Sheng Wu. 2023. Socratic pretraining: Question-driven pretraining for controllable summarization. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 12737–12755, Toron… [find](https://scholar.google.com/scholar?q=Artidoro+Pagnoni%2C+Alex+Fabbri%2C+Wojciech+Kryscinski%2C+and+Chien%2DSheng+Wu%2E+2023%2E+Socratic+pretraining%3A+Question%2Ddriven+pretraining+for+controllable+summarization%2E+In+Proceedings+of+the+61st+Annual+Meetin)
32. Sameer Pradhan, Alessandro Moschitti, Nianwen Xue, Hwee Tou Ng, Anders Björkelund, Olga Uryupina, Yuchen Zhang, and Zhi Zhong. 2013. Towards robust linguistic analysis using OntoNotes. In Proceedings of the Seventeenth Conference on Computational Natural Language Learning, pages 143–152, Sofia, Bulg… [find](https://scholar.google.com/scholar?q=Sameer+Pradhan%2C+Alessandro+Moschitti%2C+Nianwen+Xue%2C+Hwee+Tou+Ng%2C+Anders+Bj%C3%B6rkelund%2C+Olga+Uryupina%2C+Yuchen+Zhang%2C+and+Zhi+Zhong%2E+2013%2E+Towards+robust+linguistic+analysis+using+OntoNotes%2E+In+Proceedings+)
33. Xiao Pu, Mingqi Gao, and Xiaojun Wan. 2023. Summarization is (almost) dead. arXiv preprint arXiv:2309.09558. — [arXiv:2309.09558](https://arxiv.org/abs/2309.09558) [find](https://scholar.google.com/scholar?q=Xiao+Pu%2C+Mingqi+Gao%2C+and+Xiaojun+Wan%2E+2023%2E+Summarization+is+%28almost%29+dead%2E+arXiv+preprint+arXiv%3A2309%2E09558%2E+%E2%80%94+%5BarXiv%3A2309%2E09558%5D%28https%3A%2F%2Farxiv%2Eorg%2Fabs%2F2309%2E09558%29)
34. Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020. [Exploring the limits of transfer learning with a unified text-to-text transformer](https://md.research.elamur.ai/free/en/papers/raffel-2019-exploring-limits-transfer-learning-unified/). J. Mach. Learn. Res., 21(1).
35. Fabian Retkowski and Alexander Waibel. 2025. Zero-shot strategies for length-controllable summarization. In Findings of the Association for Computational Linguistics: NAACL 2025, pages 551–572, Albuquerque, New Mexico. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Fabian+Retkowski+and+Alexander+Waibel%2E+2025%2E+Zero%2Dshot+strategies+for+length%2Dcontrollable+summarization%2E+In+Findings+of+the+Association+for+Computational+Linguistics%3A+NAACL+2025%2C+pages+551%E2%80%93572%2C+Albuqu)
36. Christopher D. Rosin. 2011. Multi-armed bandits with episode context. Annals of Mathematics and Artificial Intelligence, 61(3):203–230. [find](https://scholar.google.com/scholar?q=Christopher+D%2E+Rosin%2E+2011%2E+Multi%2Darmed+bandits+with+episode+context%2E+Annals+of+Mathematics+and+Artificial+Intelligence%2C+61%283%29%3A203%E2%80%93230%2E)
37. Sangwon Ryu, Heejin Do, Daehui Kim, Hwanjo Yu, Dongwoo Kim, Yunsu Kim, Gary Lee, and Jungseul Ok. 2026. [Exploring iterative controllable summarization with large language models](https://md.research.elamur.ai/free/en/papers/ryu-2026-exploring-iterative-controllable-summarization-large/). In Findings of the Association for Computational Linguistics: EACL 2026, pages 512–528, Rabat, Morocco. Association for C…
38. Sangwon Ryu, Heejin Do, Yunsu Kim, Gary Lee, and Jungseul Ok. 2024a. Multi-dimensional optimization for text summarization via reinforcement learning. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5858–5871, Bangkok, Thailan… [find](https://scholar.google.com/scholar?q=Sangwon+Ryu%2C+Heejin+Do%2C+Yunsu+Kim%2C+Gary+Lee%2C+and+Jungseul+Ok%2E+2024a%2E+Multi%2Ddimensional+optimization+for+text+summarization+via+reinforcement+learning%2E+In+Proceedings+of+the+62nd+Annual+Meeting+of+the+)
39. Sangwon Ryu, Heejin Do, Yunsu Kim, Gary Geunbae Lee, and Jungseul Ok. 2024b. Key-element-informed sllm tuning for document summarization. In Interspeech 2024, pages 1940–1944. [find](https://scholar.google.com/scholar?q=Sangwon+Ryu%2C+Heejin+Do%2C+Yunsu+Kim%2C+Gary+Geunbae+Lee%2C+and+Jungseul+Ok%2E+2024b%2E+Key%2Delement%2Dinformed+sllm+tuning+for+document+summarization%2E+In+Interspeech+2024%2C+pages+1940%E2%80%931944%2E)
40. Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, Timothy Lillicrap, and David Silver. 2020. Mastering atari, go, chess and shogi by planning with a learned model. Nature, 588(7839):604–… [find](https://scholar.google.com/scholar?q=Julian+Schrittwieser%2C+Ioannis+Antonoglou%2C+Thomas+Hubert%2C+Karen+Simonyan%2C+Laurent+Sifre%2C+Simon+Schmitt%2C+Arthur+Guez%2C+Edward+Lockhart%2C+Demis+Hassabis%2C+Thore+Graepel%2C+Timothy+Lillicrap%2C+and+David+Silver%2E)
41. Abigail See, Peter J. Liu, and Christopher D. Manning. 2017. Get to the point: Summarization with pointer-generator networks. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1073– 1083, Vancouver, Canada. Association for Compu… [find](https://scholar.google.com/scholar?q=Abigail+See%2C+Peter+J%2E+Liu%2C+and+Christopher+D%2E+Manning%2E+2017%2E+Get+to+the+point%3A+Summarization+with+pointer%2Dgenerator+networks%2E+In+Proceedings+of+the+55th+Annual+Meeting+of+the+Association+for+Computati)
42. David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, Yutian Chen, Timothy Lillicrap, Fan Hui, Laurent Sifre, George van den Driessche, Thore Graepel, and Demis Hassabis. 2017. Mastering the game of go… [find](https://scholar.google.com/scholar?q=David+Silver%2C+Julian+Schrittwieser%2C+Karen+Simonyan%2C+Ioannis+Antonoglou%2C+Aja+Huang%2C+Arthur+Guez%2C+Thomas+Hubert%2C+Lucas+Baker%2C+Matthew+Lai%2C+Adrian+Bolton%2C+Yutian+Chen%2C+Timothy+Lillicrap%2C+Fan+Hui%2C+Laurent)
43. Hwanjun Song, Taewon Yun, Yuho Lee, Jihwan Oh, Gihun Lee, Jason Cai, and Hang Su. 2025. Learning to summarize from LLM-generated feedback. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volum… [find](https://scholar.google.com/scholar?q=Hwanjun+Song%2C+Taewon+Yun%2C+Yuho+Lee%2C+Jihwan+Oh%2C+Gihun+Lee%2C+Jason+Cai%2C+and+Hang+Su%2E+2025%2E+Learning+to+summarize+from+LLM%2Dgenerated+feedback%2E+In+Proceedings+of+the+2025+Conference+of+the+Nations+of+the+A)
44. Hugo Touvron et al. 2023. [Llama: Open and efficient foundation language models](https://md.research.elamur.ai/free/en/papers/touvron-2023-llama-open-efficient-foundation-language/). Preprint, arXiv:2302.13971.
45. Ashok Urlana, Pruthwik Mishra, Tathagato Roy, and Rahul Mishra. 2024. Controllable text summarization: Unraveling challenges, approaches, and prospects - a survey. In Findings of the Association for Computational Linguistics: ACL 2024, pages 1603–1623, Bangkok, Thailand. Association for Computationa… [find](https://scholar.google.com/scholar?q=Ashok+Urlana%2C+Pruthwik+Mishra%2C+Tathagato+Roy%2C+and+Rahul+Mishra%2E+2024%2E+Controllable+text+summarization%3A+Unraveling+challenges%2C+approaches%2C+and+prospects+%2D+a+survey%2E+In+Findings+of+the+Association+for+C)
46. Ziyu Wan, Xidong Feng, Muning Wen, Stephen Marcus Mcaleer, Ying Wen, Weinan Zhang, and Jun Wang. 2024. AlphaZero-like tree-search can guide large language model decoding and training. In Proceedings of the 41st International Conference on Machine Learning, volume 235 of Proceedings of Machine Learni… [find](https://scholar.google.com/scholar?q=Ziyu+Wan%2C+Xidong+Feng%2C+Muning+Wen%2C+Stephen+Marcus+Mcaleer%2C+Ying+Wen%2C+Weinan+Zhang%2C+and+Jun+Wang%2E+2024%2E+AlphaZero%2Dlike+tree%2Dsearch+can+guide+large+language+model+decoding+and+training%2E+In+Proceedings+o)
47. Bin Wang, Zhengyuan Liu, and Nancy Chen. 2023. Instructive dialogue summarization with query aggregations. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 7630–7653, Singapore. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Bin+Wang%2C+Zhengyuan+Liu%2C+and+Nancy+Chen%2E+2023%2E+Instructive+dialogue+summarization+with+query+aggregations%2E+In+Proceedings+of+the+2023+Conference+on+Empirical+Methods+in+Natural+Language+Processing%2C+pa)
48. Yuxi Xie, Anirudh Goyal, Wenyue Zheng, Min-Yen Kan, Timothy P. Lillicrap, Kenji Kawaguchi, and Michael Shieh. 2024. Monte carlo tree search boosts reasoning via iterative preference learning. Preprint, arXiv:2405.00451. [find](https://scholar.google.com/scholar?q=Yuxi+Xie%2C+Anirudh+Goyal%2C+Wenyue+Zheng%2C+Min%2DYen+Kan%2C+Timothy+P%2E+Lillicrap%2C+Kenji+Kawaguchi%2C+and+Michael+Shieh%2E+2024%2E+Monte+carlo+tree+search+boosts+reasoning+via+iterative+preference+learning%2E+Preprint)
49. Ruochen Xu, Song Wang, Yang Liu, Shuohang Wang, Yichong Xu, Dan Iter, Pengcheng He, Chenguang Zhu, and Michael Zeng. 2023. LMGQS: A large-scale dataset for query-focused summarization. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 14764–14776, Singapore. Association… [find](https://scholar.google.com/scholar?q=Ruochen+Xu%2C+Song+Wang%2C+Yang+Liu%2C+Shuohang+Wang%2C+Yichong+Xu%2C+Dan+Iter%2C+Pengcheng+He%2C+Chenguang+Zhu%2C+and+Michael+Zeng%2E+2023%2E+LMGQS%3A+A+large%2Dscale+dataset+for+query%2Dfocused+summarization%2E+In+Findings+of+)
50. An Yang et al. 2024. Qwen2.5 technical report. arXiv preprint arXiv:2412.15115. [find](https://scholar.google.com/scholar?q=An+Yang+et+al%2E+2024%2E+Qwen2%2E5+technical+report%2E+arXiv+preprint+arXiv%3A2412%2E15115%2E)
51. Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan. 2023. Tree of thoughts: Deliberate problem solving with large language models. In Advances in Neural Information Processing Systems, volume 36, pages 11809–11822. Curran Associates, Inc. [find](https://scholar.google.com/scholar?q=Shunyu+Yao%2C+Dian+Yu%2C+Jeffrey+Zhao%2C+Izhak+Shafran%2C+Tom+Griffiths%2C+Yuan+Cao%2C+and+Karthik+Narasimhan%2E+2023%2E+Tree+of+thoughts%3A+Deliberate+problem+solving+with+large+language+models%2E+In+Advances+in+Neural+)
52. Xiao Yu, Maximillian Chen, and Zhou Yu. 2023. Prompt-based Monte-Carlo tree search for goal-oriented dialogue policy planning. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 7101–7125, Singapore. Association for Computational Linguistics. [find](https://scholar.google.com/scholar?q=Xiao+Yu%2C+Maximillian+Chen%2C+and+Zhou+Yu%2E+2023%2E+Prompt%2Dbased+Monte%2DCarlo+tree+search+for+goal%2Doriented+dialogue+policy+planning%2E+In+Proceedings+of+the+2023+Conference+on+Empirical+Methods+in+Natural+Lan)
53. Di Zhang, Xiaoshui Huang, Dongzhan Zhou, Yuqiang Li, and Wanli Ouyang. 2024a. Accessing gpt-4 level mathematical olympiad solutions via monte carlo tree self-refine with llama-3 8b. Preprint, arXiv:2406.07394. [find](https://scholar.google.com/scholar?q=Di+Zhang%2C+Xiaoshui+Huang%2C+Dongzhan+Zhou%2C+Yuqiang+Li%2C+and+Wanli+Ouyang%2E+2024a%2E+Accessing+gpt%2D4+level+mathematical+olympiad+solutions+via+monte+carlo+tree+self%2Drefine+with+llama%2D3+8b%2E+Preprint%2C+arXiv%3A24)
54. Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. 2020. Bertscore: Evaluating text generation with bert. In Proceedings of the International Conference on Learning Representations. [find](https://scholar.google.com/scholar?q=Tianyi+Zhang%2C+Varsha+Kishore%2C+Felix+Wu%2C+Kilian+Q%2E+Weinberger%2C+and+Yoav+Artzi%2E+2020%2E+Bertscore%3A+Evaluating+text+generation+with+bert%2E+In+Proceedings+of+the+International+Conference+on+Learning+Represen)
55. Tianyi Zhang, Faisal Ladhak, Esin Durmus, Percy Liang, Kathleen McKeown, and Tatsunori B. Hashimoto. 2024b. [Benchmarking large language models for news summarization](https://md.research.elamur.ai/free/en/papers/zhang-2024-benchmarking-large-language-models-news/). Transactions of the Association for Computational Linguistics, 12:39–57.
56. Yusen Zhang, Yang Liu, Ziyi Yang, Yuwei Fang, Yulong Chen, Dragomir Radev, Chenguang Zhu, Michael Zeng, and Rui Zhang. 2023. [MACSum: Controllable summarization with mixed attributes](https://md.research.elamur.ai/free/en/papers/zhang-2023-macsum-controllable-summarization-mixed-attributes/). Transactions of the Association for Computational Linguistics, 11:787–803.
57. Yusen Zhang, Ansong Ni, Ziming Mao, Chen Henry Wu, Chenguang Zhu, Budhaditya Deb, Ahmed Awadallah, Dragomir Radev, and Rui Zhang. 2022. Summ n : A multi-stage summarization framework for long input dialogues and documents. In Proceedings of the 60th Annual Meeting of the Association for Computationa… [find](https://scholar.google.com/scholar?q=Yusen+Zhang%2C+Ansong+Ni%2C+Ziming+Mao%2C+Chen+Henry+Wu%2C+Chenguang+Zhu%2C+Budhaditya+Deb%2C+Ahmed+Awadallah%2C+Dragomir+Radev%2C+and+Rui+Zhang%2E+2022%2E+Summ+n+%3A+A+multi%2Dstage+summarization+framework+for+long+input+di)
58. Ming Zhong, Da Yin, Tao Yu, Ahmad Zaidi, Mutethia Mutuma, Rahul Jha, Ahmed Hassan Awadallah, Asli Celikyilmaz, Yang Liu, Xipeng Qiu, and Dragomir Radev. 2021. [QMSum: A new benchmark for query-based multi-domain meeting summarization](https://md.research.elamur.ai/free/en/papers/zhong-2021-qmsum-new-benchmark-query-based/). In Proceedings of the 2021 Conference of the North American Chapte…

*Updated 1 November 2026*