SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization
Open accessAbstract
This paper introduces the SAMSum Corpus, a new dataset with abstractive dialogue summaries.We investigate the challenges it poses for automated summarization by testing several models and comparing their results with those obtained on a corpus of news articles.We show that model-generated summaries of dialogues achieve higher ROUGE scores than the model-generated summaries of news -in contrast with human evaluators' judgement.This suggests that a challenging task of abstractive dialogue summarization requires dedicated models and non-standard quality measures.To our knowledge, our study is the first attempt to introduce a high-quality chatdialogues corpus, manually annotated with abstractive summarizations, which can be used by the research community for further studies.
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Previous research finds taking the longest sentence as dialogue summarization is a good baseline.
“Previous research finds taking the longest sentence20 as dialogue summarization is a good baseline.”
From Aci-bench: a Novel Ambient Clinical Intelligence Dataset for Benchmarking Automatic Visit Note Generation (2023) · cites this paper as [20]
Section 3 (Dialogues baselines) calls LONGEST-3 the best dialogue baseline, supporting the general idea that selecting long dialogue utterances is useful. But LONGEST-3 selects the three longest utterances, not the single longest sentence claimed here. (LLM, conf=0.88)
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“We also include its two variants: (1) a version with continued pre-training on PubMed abstract 24 , aimed at learning domain-specific language and knowledge, and (2) a version fine-tuned on the SAMSum corpus 25 , designed to enhance the model’s performance on conversational summarization tasks.”
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Blanco-Justicia, A., Jebreel, N., Manzanares-Salor, B., Sánchez, D., Domingo-Ferrer, J., Collell, G., & Eeik Tan, K. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-024-11078-6 · Cited by 31 · see citing works
CSDS: A Fine-Grained Chinese Dataset for Customer Service Dialogue Summarization
Lin, H., Ma, L., Zhu, J., Xiang, L., Zhou, Y., Zhang, J., & Zong, C. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.365 · Cited by 32 · see citing works
ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization
Wang, J., Meng, F., Lu, Z., Zheng, D., Li, Z., Qu, J., & Zhou, J. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.526 · Cited by 35 · see citing works
A Bag of Tricks for Dialogue Summarization
Khalifa, M., Ballesteros, M., & McKeown, K. R. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.631 · Cited by 29 · see citing works
Multilingual Large Language Models Are Not (Yet) Code-Switchers
Zhang, R., Cahyawijaya, S., Cruz, J. C. B., Winata, G. I., & Aji, A. F. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.774 · Cited by 30 · see citing works
Laskar, M. T. R., Fu, X.-Y., Chen, C., & Shashi Bhushan, T. N. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-industry.33 · Cited by 35 · see citing works
MeetingBank: A Benchmark Dataset for Meeting Summarization
Hu, Y., Ganter, T., Deilamsalehy, H., Dernoncourt, F., Foroosh, H., & Liu, F. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.906 · Cited by 40 · see citing works
QAConv: Question Answering on Informative Conversations
Wu, C.-S., Madotto, A., Liu, W., Fung, P., & Xiong, C. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.370 · Cited by 26 · see citing works
Shin, J., Yu, H., Moon, H., Madotto, A., & Park, J. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.302 · Cited by 30 · see citing works
From Softmax to Nucleusmax: A Novel Sparse Language Model for Chinese Radiology Report Summarization
Zhao, S., Li, Q., Yang, Y., Wen, J., & Luo, W. (2023) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3596219 · Cited by 26 · see citing works
Improving the Faithfulness of Abstractive Summarization via Entity Coverage Control
Zhang, H., Yavuz, S., Kryściński, W., Hashimoto, K., & Zhou, Y. (2022) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2022.findings-naacl.40 · Cited by 28 · see citing works
Reason first, then respond: Modular Generation for Knowledge-infused Dialogue
Adolphs, L., Shuster, K., Urbanek, J., Szlam, A., & Weston, J. (2022) · Findings of the Association for Computational Linguistics: EMNLP 2022 · doi:10.18653/v1/2022.findings-emnlp.527 · Cited by 25 · see citing works
Other Roles Matter! Enhancing Role-Oriented Dialogue Summarization via Role Interactions
Lin, H., Zhu, J., Lǚ, X., Zhou, Y., Zhang, J., & Zong, C. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.182 · Cited by 25 · see citing works
Instance-Aware Prompt Learning for Language Understanding and Generation
Jin, F., Lu, J., Zhang, J., & Zong, C. (2023) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3604613 · Cited by 27 · see citing works
Summ^N: A Multi-Stage Summarization Framework for Long Input Dialogues and DocumentsPreprint
Zhang, Y., Ni, A., Mao, Z., Wu, C. H., Zhu, C., Deb, B., Awadallah, A. H., Radev, D., & Zhang, R. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.10150 · Cited by 33 · see citing works
Does Pretraining for Summarization Require Knowledge Transfer?
Krishna, K., Bigham, J. P., & Lipton, Z. C. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.273 · Cited by 22 · see citing works
A Thorough Evaluation of Task-Specific Pretraining for Summarization
Rothe, S., Maynez, J., & Narayan, S. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.12 · Cited by 24 · see citing works
TWEETSUMM - A Dialog Summarization Dataset for Customer Service
Feigenblat, G., Gunasekara, C., Sznajder, B., Joshi, S., Konopnicki, D., & Aharonov, R. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.24 · Cited by 22 · see citing works
Pre-Training to Learn in Context
Gu, Y., Dong, L., Wei, F., & Huang, M. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.267 · Cited by 33 · see citing works
Molla, C., Mani, L., Bhuiyan, M. R. I., & Hossain, R. (2023) · MIGRATION LETTERS · doi:10.59670/ml.v20is9.4918 · Cited by 22 · see citing works
Baichuan2-Sum: Instruction Finetune Baichuan2-7B Model for Dialogue Summarization
Xiao, J., Chen, Y., Ou, Y., Yu, H., Shu, K., & Xiao, Y. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650513 · Cited by 26 · see citing works
Can language representation models think in bets?
Tang, Z., & Kejriwal, M. (2023) · Royal Society Open Science · doi:10.1098/rsos.221585 · Cited by 21 · see citing works
Low-Resource Dialogue Summarization with Domain-Agnostic Multi-Source Pretraining
Zou, Y., Zhu, B., Hu, X., Gui, T., & Zhang, Q. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.7 · Cited by 22 · see citing works
An Analysis of Abstractive Text Summarization Using Pre-trained Models
Rehman, T., Das, S. K., Sanyal, D. K., & Chattopadhyay, S. (2022) · Algorithms for intelligent systems · doi:10.1007/978-981-19-1657-1_21 · Cited by 22 · see citing works
Simulated Chats for Building Dialog Systems: Learning to Generate Conversations from Instructions
Mohapatra, B., Pandey, G. S., Contractor, D., & Joshi, S. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.103 · Cited by 20 · see citing works
Bae, S., Ko, J., Song, H., & Yun, S.-Y. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.362 · Cited by 28 · see citing works
Exploring LLM-powered multi-session human-robot interactions with university students
Mauliana, M., Ashok, A., Czernochowski, D., & Berns, K. (2025) · Frontiers in Robotics and AI · doi:10.3389/frobt.2025.1585589 · Cited by 23 · see citing works
Hierarchical Summarization for Longform Spoken Dialog
Li, D., Chen, T., Tung, A., & Chilton, L. B. (2021) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3472749.3474771 · Cited by 19 · see citing works
Ben Abacha, A., Yim, W.-W., Adams, G., Snider, N., & Yetisgen, M. (2023) · Proceedings of the 5th Clinical Natural Language Processing Workshop · doi:10.18653/v1/2023.clinicalnlp-1.52 · Cited by 20 · see citing works
Tang, Y., Puduppully, R., Liu, Z., & Chen, N. F. (2023) · Workshop on New Frontiers in Summarization (NewSum) · doi:10.18653/v1/2023.newsum-1.6 · Cited by 21 · see citing works
Sparsing and Smoothing for the seq2seq Models
Zhao, S., Liang, Z., Wen, J., & Chen, J. (2022) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2022.3207982 · Cited by 18 · see citing works
Abstractive video lecture summarization: applications and future prospects
Benedetto, I., Quatra, M. L., Cagliero, L., Canale, L., & Farinetti, L. (2023) · Education and Information Technologies · doi:10.1007/s10639-023-11855-w · Cited by 18 · see citing works
DialSummEval: Revisiting Summarization Evaluation for Dialogues
Gao, M., & Wan, X. (2022) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2022.naacl-main.418 · Cited by 17 · see citing works
Topic-Oriented Dialogue Summarization
Lin, H., Zhu, J., Lǚ, X., Zhai, F., Zhou, Y., Zhang, J., & Zong, C. (2023) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2023.3271118 · Cited by 17 · see citing works
Dialogue Summarization with Static-Dynamic Structure Fusion Graph
Gao, S., Cheng, X., Li, M., Chen, X., Li, J., Zhao, D., & Yan, R. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.775 · Cited by 17 · see citing works
Hierarchical Speaker-Aware Sequence-to-Sequence Model for Dialogue Summarization
Lei, Y., Yan, Y., Zeng, Z., He, K., Zhang, X., & Xu, W. (2021) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp39728.2021.9414547 · Cited by 17 · see citing works
DDRel: A New Dataset for Interpersonal Relation Classification in Dyadic Dialogues
Jia, Q., Huang, H., & Zhu, K. Q. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i14.17551 · Cited by 17 · see citing works
Using Question Answering Rewards to Improve Abstractive Summarization
Gunasekara, C., Feigenblat, G., Sznajder, B., Aharonov, R., & Joshi, S. (2021) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2021.findings-emnlp.47 · Cited by 17 · see citing works
MSAMSum: Towards Benchmarking Multi-lingual Dialogue Summarization
Feng, X., Feng, X., & Qin, B. (2022) · DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering (DialDoc) · doi:10.18653/v1/2022.dialdoc-1.1 · Cited by 16 · see citing works
Analyzing and Evaluating Faithfulness in Dialogue Summarization
Wang, B., Zhang, C., Zhang, Y., Chen, Y., & Li, H. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.325 · Cited by 17 · see citing works
- Authors
- Gliwa, B., Mochol, I., Biesek, M., & Wawer, A.
- Affiliations
- Samsung (Poland) (PL)
- Venue
- Proceedings of the 2nd Workshop on New Frontiers in Summarization
- Published
- 2019
- Pages
- 70-79
- OpenAlex
- W2989743967
- Cited by
- 424
- References
- 24
- Citations by year
- 2026: 30 · 2025: 72 · 2024: 52 · 2023: 125 · 2022: 72 · 2021: 67 · 2020: 6
- FWCI
- 13.57
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Workshop on New Frontiers in Summarization (NewSum) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
- Keywords
- abstractive summarization, dialogue summarization, news summarization
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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