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SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization
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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 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]
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
Jakubův, J., Chvalovský, K., Goertzel, Z., et al. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,648
Interrater reliability: The kappa statistic
McHugh, M. L. (2012) · Biochemia Medica · doi:10.11613/bm.2012.031 · Cited by 19,877
(2004) · Cited by 9,718
Get To The Point: Summarization with Pointer-Generator Networks
See, A., Liu, P. J., & Manning, C. D. (2017) · Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p17-1099 · Cited by 3,775
A Neural Attention Model for Abstractive Sentence Summarization
Rush, A. M., Chopra, S., & Weston, J. (2015) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d15-1044 · Cited by 2,932
Teaching Machines to Read and ComprehendPreprint
Hermann, K. M., Kočiský, T., Grefenstette, E., Espeholt, L., Kay, W., Suleyman, M., & Blunsom, P. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.03340 · Cited by 2,855
fairseq: A Fast, Extensible Toolkit for Sequence Modeling
Ott, M., Edunov, S., Baevski, A., Fan, A., Gross, S., Ng, N., Grangier, D., & Auli, M. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-4009 · Cited by 2,475
Abstractive Text Summarization using Sequence-to-sequence RNNs and Beyond
Nallapati, R., Zhou, B., dos Santos, C., Gulcehre, C., & Xiang, B. (2016) · Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning · doi:10.18653/v1/k16-1028 · Cited by 2,361
Abstractive Sentence Summarization with Attentive Recurrent Neural Networks
Chopra, S., Auli, M., & Rush, A. M. (2016) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n16-1012 · Cited by 896
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Can LMs Generalize to Future Data? An Empirical Analysis on Text Summarization
Cheang, C., Chan, H. P., Wong, D. F., Liu, X., Li, Z., Sun, Y., Liu, S., & Chao, L. S. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.1007 · Cited by 9
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Liu, X., Tang, Z., Dong, P., Li, Z., Liu, Y., Li, B., Hu, X., & Chu, X. (2025) · arXiv · doi:10.48550/arxiv.2502.00299 · Cited by 4
Motghare, M., Agarwal, M., & Agrawal, A. (2025) · Computers · doi:10.3390/computers14120508 · Cited by 5
Chen, Y., Gao, Y., Yu, S., Zhao, C., & Lu, Y. (2025) · Journal of Industrial Integration and Management · doi:10.1142/s2424862225300042 · Cited by 5
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Feng, X., Feng, X., Du, X., Kan, M., & Qin, B. (2024) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2024.3414313 · Cited by 7
Enhancing Medical Dialogue Summarization: A MediExtract Distillation Framework
Liu, X., Huang, M., Rusnachenko, N., Ive, J., Chang, J. M., & Zhang, J. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10822640 · Cited by 4
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Lee, H., Yun, J.-W., Choi, H., Joe, S., & Gwon, Y. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-1270 · Cited by 4
Learning-by-Narrating: Narrative Pre-Training for Zero-Shot Dialogue Comprehension
Zhao, C., Yao, W., Yu, D., Song, K., Yu, D., & Chen, J. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-short.23 · Cited by 5
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Ye, S., Jang, J., Kim, D., Jo, Y., & Seo, M. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.822 · Cited by 9
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Bertsch, A., Neubig, G., & Gormley, M. R. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.355 · Cited by 4
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Li, C., Huber, P., Wen, X., Amblard, M., Braud, C., & Carenini, G. (2023) · Findings of the Association for Computational Linguistics: EACL · doi:10.18653/v1/2023.findings-eacl.194 · Cited by 4
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Foysal, A. A., & Böck, R. (2023) · AI · doi:10.3390/ai4040049 · Cited by 6
Yamashita, S., Mochizuki, S., Kawasaki, K., Kubota, T., Ogawa, K., Baba, J., & Higashinaka, R. (2023) · International Conference on Human-Agent Interaction (HAI) · doi:10.1145/3623809.3623889 · Cited by 4
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Liu, Y., Fabbri, A. R., Zhao, Y., Liu, P., Joty, S., Wu, C.-S., Xiong, C., & Radev, D. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.1018 · Cited by 14
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Wang, S., Ma, H., Zhang, Y., Ma, J., & He, L. J. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10650220 · Cited by 4
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Manuvinakurike, R., Sahay, S., Chen, W., & Nachman, L. (2021) · Annual Meeting of the Special Interest Group on Discourse and Dialogue · doi:10.18653/v1/2021.sigdial-1.55 · Cited by 6
Lee, D., Lim, J., Whang, T., Lee, C., Cho, S., Park, M., & Lim, H. (2021) · Workshop on New Frontiers in Summarization (NewSum) · doi:10.18653/v1/2021.newsum-1.8 · Cited by 4
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Yang, M., Ng, S.-K., & Fu, J. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3542301 · Cited by 4
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Yao, J., Li, H., Liu, Y., Ray, S., Cheng, Y., Zhang, Q., Du, K., Lu, S., & Jiang, J. (2026) · ACM Transactions on Computer Systems · doi:10.1145/3790254 · Cited by 3
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Gu, Y., Ke, P., Zhu, X., & Huang, M. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.105 · Cited by 8
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He, H., Liu, Q., Xu, L., Shivade, C., Zhang, Y., Srinivasan, S., & Kirchhoff, K. (2025) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v39i22.34575 · Cited by 8
End-to-End Long Document Summarization using Gradient Caching
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Zhang, H., Liu, S., Li, Y., Ren, W., Tong, H., Cao, J., & Zhou, Q. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3269013 · Cited by 3
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Zou, Y., Song, K., Tan, X., Fu, Z., Zhang, Q., Li, D., & Gui, T. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.798 · Cited by 3
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Frugal Prompting for Dialog Models
Santra, B., Basak, S., De, A., Gupta, M., & Goyal, P. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.290 · Cited by 3
Topic-Informed Dialogue Summarization using Topic Distribution and Prompt-based Modeling
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Enhancing Abstractiveness of Summarization Models through Calibrated Distillation
Song, H., Shalyminov, I., Su, H., Singh, S., Yao, K., & Mansour, S. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.468 · Cited by 4
Ko, J., Park, S., Kim, Y., Ahn, S., Chang, D.-S., Ahn, E., & Yun, S.-Y. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.404 · Cited by 10
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BackgroundAssessing Privacy Risks in Language Models: A Case Study on Summarization Tasks
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Zhuang, Y., Gong, C., Zheng, Z., Wu, F., & Chen, G. (2026) · IEEE Transactions on Networking · doi:10.1109/ton.2026.3658387 · Cited by 1
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Sroch, R. (2021) · International Conference on Algorithms, Computing and Artificial Intelligence (ACAI) · doi:10.1145/3508546.3508640 · Cited by 1
SUMBot: Summarizing Context in Open-Domain Dialogue Systems
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Xing, Y., & Gulla, J. A. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-35320-8_35 · Cited by 1
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Ravaut, M., Joty, S., & Chen, N. F. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.529 · Cited by 1
Annotating and Detecting Fine-grained Factual Errors for Dialogue Summarization
Zhu, R., Qi, J., & Lau, J. H. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.377 · Cited by 7
Metadata
- 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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