---
title: "MSMO: Multimodal Summarization with Multimodal Output (2018)"
description: "It introduces MSMO, a task where a system receives an article plus an unaligned set of images and… 39 references, cited by 66 papers here."
authors: ["Junnan Zhu", "Haoran Li", "Tianshang Liu", "Yu Zhou", "Jiajun Zhang", "Chengqing Zong"]
year: 2018
venue: "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/D18-1448/"
doi: "10.18653/v1/d18-1448"
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/zhu-2018-msmo-multimodal-summarization-multimodal-output/"
---
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# MSMO: Multimodal Summarization with Multimodal Output

**Authors:** Junnan Zhu, [Haoran Li](https://md.research.elamur.ai/free/en/authors/haoran-li/), Tianshang Liu, [Yu Zhou](https://md.research.elamur.ai/free/en/authors/yu-zhou/), [Jiajun Zhang](https://md.research.elamur.ai/free/en/authors/jiajun-zhang/) and [Chengqing Zong](https://md.research.elamur.ai/free/en/authors/chengqing-zong/) · **Year:** 2018 · **Venue:** [Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing](https://md.research.elamur.ai/free/en/venues/emnlp/) ([EMNLP 2018](https://md.research.elamur.ai/free/en/venues/emnlp/2018/)) · **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/D18-1448/) · [PDF](https://aclanthology.org/D18-1448.pdf) · [DOI 10.18653/v1/d18-1448](https://doi.org/10.18653/v1/d18-1448) · [OpenAlex](https://openalex.org/W2890721473)

**Citations:** 179 citations (OpenAlex, 2026-10-14) · [66](https://md.research.elamur.ai/free/en/papers/zhu-2018-msmo-multimodal-summarization-multimodal-output/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
The paper argues that summaries of multimedia news should be multimodal themselves. It introduces MSMO, a task where a system receives an article plus an unaligned set of images and must return a short text summary together with the single most relevant image. The authors built a large corpus from the Daily Mail website and annotated some pictorial summaries. Their model extends the pointer-generator network with a VGG19 image encoder, a visual attention layer, and a multimodal attention layer that weights textual against visual context during decoding. A visual coverage vector reduces repeated attention to the same images, and its final state determines which image is chosen. To evaluate outputs, they propose MMAE, an automatic metric that learns to combine text salience, image salience, and image-text relevance. MMAE agrees with human ratings far better than any single component metric and transfers to an unseen system. This gives future multimodal summarization work a dataset, a baseline, and a joint evaluation method.

## Key points
- In a user study, adding an image to the text summary raised user satisfaction with informativeness by 12.4% compared with text alone.
- The architecture has four parts: a BiLSTM text encoder, a VGG19 image encoder, a multimodal attention layer, and an LSTM decoder. Three visual attention variants were tested: ATG (global fc7 features), ATL (local pool5 features), and HAN (hierarchical attention over local features).
- The final MMAE is a linear regression over ROUGE-L, MAX sim and IP. On the test set it reaches Pearson r ≈ 0.665, while the best single metric (IP) reaches about 0.598 and ROUGE-L about 0.349.
- On a new baseline system (LexRank sentence extraction plus a random image), MMAE kept a correlation of about 0.635 with human judgments, versus about 0.322 for ROUGE-L.
- On 9,851 test articles, ATG achieved the best MMAE score (3.35). All attention models beat the extractive baseline GR, which scored 3.20.

*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

- [Chinese Academy of Sciences](https://md.research.elamur.ai/free/en/government/chinese-academy-of-sciences/) — Government body, Beijing, China · 68 works · 22,753 citations
- [Institute of Automation](https://md.research.elamur.ai/free/en/labs/institute-of-automation/) — Research institute, Beijing, China · 20 works · 13,875 citations
- [University of Chinese Academy of Sciences](https://md.research.elamur.ai/free/en/universities/university-of-chinese-academy-of-sciences/) — University, Beijing, China · 32 works · 7,615 citations

Also on the byline: [Center for Excellence in Brain Science and Intelligence Technology](https://ror.org/00vpwhm04).

📚 Shelves (2): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 9,085 works · [👁️ Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · 1,282 works

🧭 Topics (2): [🖼️ Vision & language](https://md.research.elamur.ai/free/en/topics/multimodal/) · 269 works · [📝 Summarization](https://md.research.elamur.ai/free/en/topics/summarization/) · 159 works

## Cite

Cite as: Junnan Zhu, Haoran Li, Tianshang Liu, Yu Zhou, Jiajun Zhang and Chengqing Zong. 2018. MSMO: Multimodal Summarization with Multimodal Output. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing.

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

```bibtex
@inproceedings{zhu2018msmo,
  title = {{MSMO: Multimodal Summarization with Multimodal Output}},
  author = {Junnan Zhu, Haoran Li, Tianshang Liu, Yu Zhou, Jiajun Zhang and Chengqing Zong},
  booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
  year = {2018},
  doi = {10.18653/v1/d18-1448},
  url = {https://doi.org/10.18653/v1/d18-1448}
}
```

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*Updated 24 October 2026*