---
title: "Deep Communicating Agents for Abstractive — Celikyilmaz 2018"
description: "Neural encoder-decoder models handle short inputs well, but their performance drops when a long document must be condensed into an abstractive summary."
authors: ["Asli Celikyilmaz", "Antoine Bosselut", "Xiaodong He", "Yejin Choi"]
year: 2018
venue: "Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)"
source: "https://aclanthology.org/N18-1150/"
doi: "10.18653/v1/n18-1150"
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/celikyilmaz-2018-deep-communicating-agents-abstractive-summarization/"
---
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# Deep Communicating Agents for Abstractive Summarization

**Authors:** [Asli Celikyilmaz](https://md.research.elamur.ai/free/en/authors/asli-celikyilmaz/), [Antoine Bosselut](https://md.research.elamur.ai/free/en/authors/antoine-bosselut/), [Xiaodong He](https://md.research.elamur.ai/free/en/authors/xiaodong-he/), [Yejin Choi](https://md.research.elamur.ai/free/en/authors/yejin-choi/) · **Year:** 2018 · **Venue:** Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/N18-1150/) · [PDF](https://aclanthology.org/N18-1150.pdf) · [DOI 10.18653/v1/n18-1150](https://doi.org/10.18653/v1/n18-1150) · [OpenAlex](https://openalex.org/W2794945088)

**Citations:** 323 citations (openalex-snapshot · 2026-10-14) · [108](#cited-by) in our library, of the 140884 papers whose bibliographies we read

## Summary
Neural encoder-decoder models handle short inputs well, but their performance drops when a long document must be condensed into an abstractive summary. Celikyilmaz et al. address this by splitting the encoding work among several agents, each responsible for one paragraph. They adapt CommNet to sequence generation. Each agent first encodes its paragraph with a local bLSTM. It then passes through stacked contextual layers, where it receives the averaged final states of the other agents as a message and updates its own representation.

A single LSTM decoder uses hierarchical attention: first over the words within each agent, then over the agents. A contextual agent attention component discourages the decoder from jumping between agents too often. A multi-agent pointer mechanism lets each agent vote on which out-of-vocabulary words to copy. The model is trained end-to-end with self-critical reinforcement learning.

On CNN/DailyMail and New York Times, communicating encoders beat single-encoder and non-communicating multi-encoder baselines, and human judges rated the summaries as more focused. The work matters because it shows that agent communication is a practical way to represent long inputs.

## Key points
- The architecture combines a local bLSTM encoder, K contextual layers with message passing (messages are averages of the other agents' last hidden states), word-level and agent-level attention, and per-agent generation probabilities for copying.
- Training uses self-critical RL, together with a semantic coherence loss and intermediate sentence-level rewards intended to reduce semantically repetitive output.
- In the human evaluation on a CNN/DM subset, the multi-agent model outscored the single agent on focus (3.884 vs 3.694, p \< 0.001) and overall quality (3.682 vs 3.558, p \< 0.03). It was also preferred head-to-head on overall quality (158 vs 102 wins).
- An analysis of ROUGE-L scores against how much attention each agent received showed that summaries drawing more evenly on all agents scored higher. The multi-agent model also outperformed the single agent in every attention bin.
- In qualitative examples, the best multi-agent model (m7) captured key facts that the best single-agent model (m3) missed, and it included fewer unnecessary details.

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

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cited by

Cited by

108 papers of our library cite it (of the 140884 whose bibliographies we read); 10 of them have a page here, 98 not yet.

- [Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting](https://md.research.elamur.ai/free/en/papers/chen-2018-fast-abstractive-summarization-reinforce-selected/)
- [Multi-News: A Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model](https://md.research.elamur.ai/free/en/papers/fabbri-2019-multi-news-large-scale-multi/)
- [Bottom-Up Abstractive Summarization](https://md.research.elamur.ai/free/en/papers/gehrmann-2018-bottom-up-abstractive-summarization/)
- [Efficient Attentions for Long Document Summarization](https://md.research.elamur.ai/free/en/papers/huang-2021-efficient-attentions-long-document-summarization/)
- [Hierarchical Transformers for Multi-Document Summarization](https://md.research.elamur.ai/free/en/papers/liu-2019-hierarchical-transformers-multi-document-summarization/)
- [Pragmatically Informative Text Generation](https://md.research.elamur.ai/free/en/papers/shen-2019-pragmatically-informative-text-generation/)
- [Pay Less Attention with Lightweight and Dynamic Convolutions](https://md.research.elamur.ai/free/en/papers/wu-2019-pay-less-attention-lightweight-dynamic/)
- [Discourse-Aware Neural Extractive Text Summarization](https://md.research.elamur.ai/free/en/papers/xu-2019-discourse-aware-neural-extractive-text/)
- [HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization](https://md.research.elamur.ai/free/en/papers/zhang-2019-hibert-document-level-pre-training/)
- [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/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{celikyilmaz2018deep,
  title = {{Deep Communicating Agents for Abstractive Summarization}},
  author = {Celikyilmaz, Asli and Bosselut, Antoine and He, Xiaodong and Choi, Yejin},
  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)},
  year = {2018},
  doi = {10.18653/v1/n18-1150},
  url = {https://doi.org/10.18653/v1/n18-1150}
}
```

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