@inproceedings{liu-etal-2026-entity,
title = "Entity Blackboard: Structured Agent Memory for Long Document Summarization",
author = "Liu, Ruyi and
Zou, Guanyun and
Dai, Hongliang and
Li, Piji",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.384/",
pages = "8099--8114",
ISBN = "979-8-89176-462-0",
abstract = "Hierarchical merging is commonly used to summarize long documents by splitting the input into chunks, summarizing each chunk independently, and merging the results into a final summary. However, because each chunk is processed independently, entity state changes across chunks are not tracked, leading to coherence and consistency issues in the final summary. We propose EntityBB (Entity Blackboard), an agent memory framework that equips chunk-based LLM summarizers with structured entity memory. During sequential chunk processing, the model records and updates entity states through tool calls, while automatic safeguards and model-driven rejection limit noisy writes. Experiments on three long document summarization benchmarks show that EntityBB consistently improves over a controlled hierarchical merging baseline across ROUGE and BERTScore, while NarrativeFactScore indicates improved source-grounded factuality. Targeted human and LLM-as-a-Judge evaluations further indicate fewer entity-level issues and higher judged summary quality."
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<abstract>Hierarchical merging is commonly used to summarize long documents by splitting the input into chunks, summarizing each chunk independently, and merging the results into a final summary. However, because each chunk is processed independently, entity state changes across chunks are not tracked, leading to coherence and consistency issues in the final summary. We propose EntityBB (Entity Blackboard), an agent memory framework that equips chunk-based LLM summarizers with structured entity memory. During sequential chunk processing, the model records and updates entity states through tool calls, while automatic safeguards and model-driven rejection limit noisy writes. Experiments on three long document summarization benchmarks show that EntityBB consistently improves over a controlled hierarchical merging baseline across ROUGE and BERTScore, while NarrativeFactScore indicates improved source-grounded factuality. Targeted human and LLM-as-a-Judge evaluations further indicate fewer entity-level issues and higher judged summary quality.</abstract>
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%0 Conference Proceedings
%T Entity Blackboard: Structured Agent Memory for Long Document Summarization
%A Liu, Ruyi
%A Zou, Guanyun
%A Dai, Hongliang
%A Li, Piji
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F liu-etal-2026-entity
%X Hierarchical merging is commonly used to summarize long documents by splitting the input into chunks, summarizing each chunk independently, and merging the results into a final summary. However, because each chunk is processed independently, entity state changes across chunks are not tracked, leading to coherence and consistency issues in the final summary. We propose EntityBB (Entity Blackboard), an agent memory framework that equips chunk-based LLM summarizers with structured entity memory. During sequential chunk processing, the model records and updates entity states through tool calls, while automatic safeguards and model-driven rejection limit noisy writes. Experiments on three long document summarization benchmarks show that EntityBB consistently improves over a controlled hierarchical merging baseline across ROUGE and BERTScore, while NarrativeFactScore indicates improved source-grounded factuality. Targeted human and LLM-as-a-Judge evaluations further indicate fewer entity-level issues and higher judged summary quality.
%U https://aclanthology.org/2026.findings-emnlp.384/
%P 8099-8114
Markdown (Informal)
[Entity Blackboard: Structured Agent Memory for Long Document Summarization](https://aclanthology.org/2026.findings-emnlp.384/) (Liu et al., Findings 2026)
ACL