---
title: "Multi-View Sequence-to-Sequence Models with Conversational"
description: "Most summarization research targets single-author, well-organized texts such as news. 51 references, cited by 87 papers here."
authors: ["Jiaao Chen", "Diyi Yang"]
year: 2020
venue: "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)"
source: "https://aclanthology.org/2020.emnlp-main.336/"
doi: "10.18653/v1/2020.emnlp-main.336"
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/chen-2020-multi-view-sequence-sequence-models/"
---
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# Multi-View Sequence-to-Sequence Models with Conversational Structure for Abstractive Dialogue Summarization

**Authors:** [Jiaao Chen](https://md.research.elamur.ai/free/en/authors/jiaao-chen/) and [Diyi Yang](https://md.research.elamur.ai/free/en/authors/diyi-yang/) · **Year:** 2020 · **Venue:** [Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/) ([EMNLP 2020](https://md.research.elamur.ai/free/en/venues/emnlp/2020/)) · **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/2020.emnlp-main.336/) · [PDF](https://aclanthology.org/2020.emnlp-main.336.pdf) · [DOI 10.18653/v1/2020.emnlp-main.336](https://doi.org/10.18653/v1/2020.emnlp-main.336) · [OpenAlex](https://openalex.org/W3104257895)

**Citations:** 117 citations (OpenAlex, 2026-10-14) · [87](https://md.research.elamur.ai/free/en/papers/chen-2020-multi-view-sequence-sequence-models/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Most summarization research targets single-author, well-organized texts such as news. Chat dialogues are messier: key facts are spread across turns by different speakers and mixed with fillers, abbreviations and emoticons. Chen and Yang tackle abstractive dialogue summarization by giving the model explicit conversational structure. Each chat is automatically segmented from several perspectives:
- a topic view, using C99 segmentation over Sentence-BERT utterance embeddings;
- a stage view, using an HMM with ordered stages (openings, intentions, discussions, conclusions);
- generic global and discrete (per-utterance) views.

A conversation encoder processes each view separately. A multi-view decoder first weighs the views, then attends to tokens inside them. On the SAMSum corpus, the BART-based version outperformed earlier state-of-the-art systems in both automatic metrics and human evaluation. A manual analysis of 100 test dialogues shows that omitted content and wrong speaker attribution remain open problems. The work matters because it shows that cheap, unsupervised structure cues can improve chat summaries without expert discourse annotation.

## Key points
- Four conversation views are combined: topic (C99 + Sentence-BERT), stage (HMM with monotonic stage transitions), global, and discrete. The best model is Multi-view BART with topic + stage views.
- Evaluation uses SAMSum (Gliwa et al., 2019). Code is released at github.com/GT-SALT/Multi-View-Seq2Seq.
- In 100 sampled test dialogues, the difficulties encountered most often were resolving who or what is being referred to (33%) and shifts in speaker roles or in the language used (30%). Generic, simple chats were the easiest to summarize.
- The most common error type was missing information (37%), followed by incorrect reasoning (24%), which had the lowest ROUGE-2. Improper gendered pronouns were rare (6%) but sharply lowered ROUGE-1/2.
- Wrong speaker references co-occurred strongly with coreference difficulties. Future work targets supervised segmentation and dialogue-act structure.

*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

- [Georgia Institute of Technology](https://md.research.elamur.ai/free/en/universities/georgia-institute-of-technology/) — University, Atlanta, United States · 69 works · 22,788 citations

📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 9,086 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 8,889 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 9,296 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 9,206 works

🧭 Topics (2): [🗨️ Dialogue systems](https://md.research.elamur.ai/free/en/topics/dialogue-systems/) · 401 works · [📝 Summarization](https://md.research.elamur.ai/free/en/topics/summarization/) · 159 works

## Cite

Cite as: Jiaao Chen and Diyi Yang. 2020. Multi-View Sequence-to-Sequence Models with Conversational Structure for Abstractive Dialogue Summarization. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP).

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

```bibtex
@inproceedings{chen2020multi,
  title = {{Multi-View Sequence-to-Sequence Models with Conversational Structure for Abstractive Dialogue Summarization}},
  author = {Jiaao Chen and Diyi Yang},
  booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year = {2020},
  doi = {10.18653/v1/2020.emnlp-main.336},
  url = {https://doi.org/10.18653/v1/2020.emnlp-main.336}
}
```

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*Updated 24 October 2026*